{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入数据，查看数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('day.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "离散型特征：season/ yr/ mnth/ holiday/ weekday/ workingday/ weathresit  \n",
    "连续型特征：dteday/temp/atemp/hum/windspeed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单数据分布规律"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>连续型特征：temp/  atemp/  hum/  windspeed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c93cec320>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c93cecb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#casual\n",
    "fig=plt.figure()\n",
    "sns.distplot(df['casual'],bins=10,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c93d09da0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c93dc0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['registered'],bins=10,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c940b6518>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c940ff710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['cnt'],bins=10,kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "临时用户的数量集中在0-1000,注册用户和总用户的数量形状与正态分布类似"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c941262b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c941262e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#temp\n",
    "fig=plt.figure()\n",
    "sns.distplot(df['temp'],bins=30,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000029C94184128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000029C94262198>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c94210438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#atemp/hum\n",
    "features=['atemp','hum']\n",
    "df[features].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c942695c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c942d4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['windspeed'],bins=10,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c943afe10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c942699b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#temp和atemp箱体图\n",
    "_,axes=plt.subplots(1,2,sharey=True,figsize=(7,4))\n",
    "sns.boxplot(data=df['temp'],ax=axes[0])\n",
    "sns.violinplot(data=df['temp'],ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "离散型特征：season/ yr/ mnth/ holiday/ weekday/ workingday/ weathresit  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    188\n",
       "2    184\n",
       "1    181\n",
       "4    178\n",
       "Name: season, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['season'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c9429f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.countplot(df['season'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "四个季节的数据量基本一致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c9447f668>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c943de278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['yr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两年的数据量基本一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c94184f98>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uAqiqH1bV/RMqvwL4RlX9e+M6S4DHJlkCHMU81+0skqcD/1JVP6iqXcBngIsWu8gefr4vBNYOr9cCK1vWq6rNVbU/F7nuT61PDd9PgH9hdO3Vojokg2BakiwDzgBuaVznsCS3AjuAm6qqaT3g3cCbgYcb19mtgE8l2ThcYd7SU4GdwNXDrq/3Jnlc45q7vRz4YMsCVfUfwB8A9wDbgP+sqk81KrcJOCfJE5McBbyY/3/xaEsnVtU2GP1xBpwwobqT9kvA3y/2oAbBIknyeOCjwGVV9b2WtarqoWHXwlLgzGGTvIkk5wM7qmpjqxrzOLuqns3oDrWvS3JOw1pLgGcD76mqM4Dvs7i7FeY1XEx5AfA3jescy+iv5acAPwE8LsmrWtSqqs3AOxntyrgRuI3RrlMtgiS/zej7ec1ij20QLIIkhzMKgWuq6mOTqjvswriZtsdDzgYuSHI3ozvFnpvkrxrWo6ruHZ53MNp/fmbDcluBrXO2qtYxCobWXgR8qaq2N67zfODfqmpnVf038DHgZ1sVq6qrqurZVXUOo10cW1rVeoTtSU4CGJ53TKjuRCRZBZwPvLIanPNvEDxKScJo//LmqvqjCdSb2X3WQJLHMvpB/1qrelX11qpaWlXLGO3K+IeqavIXJUCSxyV5wu7XwAsY7XJooqq+BXwzyanDrBXAHa3qzfEKGu8WGtwDnJXkqOH/6goaHgxPcsLwfAqjg6mTeI8wuk3NquH1KuC6CdVtLsl5wFuAC6rqB02KVNUh92D0n28b8N+M/uJb3bDWzzHap/0V4Nbh8eKG9Z4FfHmotwn4nQl+X58L3NC4xlMZ7VK4Dbgd+O0JvK/TgQ3D9/TjwLGN6x0FfBv48Qn9u72d0R8Lm4APAD/WsNY/MgrS24AVjWr8yM838ERGZwttGZ6Pa1zvouH1g8B24JMNa93J6Nb9u3+//Plif0+9sliSOueuIUnqnEEgSZ0zCCSpcwaBJHXOIJCkzhkE0iIZ7mL62jnTz53E3VqlR8sgkBbPMcBrF1xKOsAYBNIcSZYN935/73Bf/WuSPD/J54d73Z+Z5G3DfeNvTnJXkjcMq78DeNrwOQPvGuY9fs5nHVwzXN0rHVC8oEyaY7iD7J2M7iJ7O/BFRlfJrmZ0k7jXMLq68wXA84AnAF8HnsTo9uM31Oi+/yR5LqNbHfw0o1s/fx54U1V9blLvRxqHWwTSj/q3qvpqVT3MKAzW1+gvpq8Cy4Zl/q6qHqyq+xjd4OzEPYz1haraOox165z1pQOGQSD9qAfnvH54zvTDjG5b/chlHpozf29j7W05aWoMAmnxPMBoV5F0UDEIpEVSVd8GPj8cZH7XgitIBwgPFktS59wikKTOGQSS1DmDQJI6ZxBIUucMAknqnEEgSZ0zCCSpcwaBJHXufwDiTKVeB+MyzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c9446a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['mnth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c94504780>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c9454ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['holiday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c9458cb70>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c94555b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['weekday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c94633320>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c945ca358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['workingday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c94672c18>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c9463db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['weathersit'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "猜测：atemp和temp之间是正先关  \n",
    "      holiday和workingday是负相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征与特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c9474ed68>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c94708b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用热度图表示不同特征之间的相关系数\n",
    "cols=df.columns\n",
    "data_corr=df.corr()\n",
    "sns.heatmap(data_corr,annot=True)   #annot表示数据之间的相关性大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c94981cf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c94966b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr=data_corr.abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "sns.heatmap(data_corr,mask=data_corr<0.5,cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instant and yr = 0.87\n",
      "instant and registered = 0.66\n",
      "instant and cnt = 0.63\n",
      "season and mnth = 0.83\n",
      "yr and registered = 0.59\n",
      "yr and cnt = 0.57\n",
      "workingday and casual = 0.52\n",
      "weathersit and hum = 0.59\n",
      "temp and atemp = 0.99\n",
      "temp and casual = 0.54\n",
      "temp and registered = 0.54\n",
      "temp and cnt = 0.63\n",
      "atemp and casual = 0.54\n",
      "atemp and registered = 0.54\n",
      "atemp and cnt = 0.63\n",
      "casual and cnt = 0.67\n",
      "registered and cnt = 0.95\n"
     ]
    }
   ],
   "source": [
    "size=data_corr.shape[0]\n",
    "col=cols.drop('dteday')    #去掉object型对象\n",
    "corr_list=[]\n",
    "threshold=0.5\n",
    "for i in range(size):\n",
    "    for j in range(i+1,size):\n",
    "        if data_corr.iloc[i,j]>threshold and data_corr.iloc[i,j]<1:\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j])\n",
    "for v,i,j in corr_list:\n",
    "    print('{} and {} = {:.2f}'.format(col[i],col[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c94966cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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22TpwD9QEmV6URziuEFc0y3+966tj+OBYPZMuyOW59w4zb2oh4YRKQY6fqKLy5KaDKf7r6HtfQzEu67MnDOGur45h6Yu7mDUun+smFvD9p/UuivvmjEMCTqG7qn6Pk3hSRQPqI4kUFtmDc8eTm+GxfGcDaR/3XIPDgUWAMbm2Y4fksL8mSLbfbQl23D1zDK/sqGDskByee+8w//r5QtwuBwMyPdRGEiiapGRqoeWPmpRHE+t2HuOVHRU8sWAK100sYHN5DStKiq0Gy1d2VCAEYDDKYkkVTcoWLLK7X9iF1sl0bruVMyHEkvb+LqV8qHOHTaMn4HM7eWHbIb41bRhJVePGzxeiajAmPxufx8nRuigLZxRxfn+/5XPmZXnxulzURxNk+9wsWlvG4/On8G9rt1tVsbf3VrOipDil+jXv0gvQpE7IGZGXZbXzROIqr++p4oLcAFOG5eIU4HI78beh/hjwds5VONmKm2XcpgC30dRK8wPgok4dMY0eg2k0iaTGH9/5lKQqOVYfIaFqNEaTvLy9ggXThnP4RMSqfN1+RREAd7+wq6kF3dtkZLMnDGHG5/ItwzR7yXIDbgJep8VxMNt5nA5YUVLMY/84yIvbK1A0id/40XRnC0+7hiul/JmU8mfAQGCSlPJOKeWdwGT0ToQ0OoC2JOm7+xiqpkfxedlevjm1kLLDdYzOzyYUV1i95RBzJhaQ5XPx8BufWMIeRYMyCXidKeXamsa4ZWS3X1HE0hd3WamvkT9ez/efLiWqaEQSquWWPDKvmKsvHkx9JGkZ+Q2ThrJ6yyGCUYWXt1fwwA3jU/Vz5xV3uoWnoz5uIZCwPU8Awzp1xHMMzRsXe2qoRyShK81EExo1wTh5WV4uGzkQKfXV9KE39vHmx1VWbvXlHRU8XFLMkdoIEaPKVl4d5NGbJuF0wn9/YwKXjRjQKifBpCUG3E4WGG7J5aPyWLS2jEHZPla+Wc5v3ion4HUyc9xgVm05yJyJLTuABwQ6TybvqOE+DbwnhFgmhPgv4F1gdaeO2AfR3oraliR9d7a2mMNE3j9Uy/k5fhKKnoLK9rut7eYl/6nNB1lZUsxVFw8my+tiUJYXIQSFAwLM/e07KJrkjmfL+PVrH7N87ngiCaXNS7zDIXA6HPzxnU8tZUeTxmh2VphuxPK/7U2hNmZ4nTi7IPjcoVdKKe9Hb6WpA+qBb0spf9npo/ZymIaqahqReJNmQWsrasDj7Nb8ZWuIJFUOn4jwyLxiQnGFITl+GqIJfXU1GGHmJT/b78btcqBqku+u2saPX95NLKlaOd3+AY+1wmZ6Xaia5MG5qZf43940CSRoUlqramO0SXNswbThKR0UzQ3f63IQ76qemJSyQzfgcnSDBcgDhnf0taf7NnnyZNlTUFVNVjfG5IqNe+Wnx8PyYE1Iljy2VV6w9K/WreSxrTIYS0oppQzHk/JIbViu33VU1oXjUtU02RBNyHg8KYOxpFQ1Tb9Xtc6fk6bJRWu2y4ZIQq7YuFfWh/X7YDQhg9GErGqISkXV96kPJ+Sxuoh1zh9XNsoVG/fKE6GYPFoXsT5PRW1Y1kcSsuSxrfLJTQescw9GE7K6MSpLHtsqR97zqjxWF5GKqsmnNh+QVQ36dlXVpKJqcuQ9r8onNx2QteG4PFIblis27pUfVzZKRdVkQyQhFUVt7eN06H/coRXXcA+WAvcYm9zAM137yfROWN0FRjrJrllgIrVZEHZX1DP5glxue2Y7o+99jac2HSSiaBwPxpESjgfjBGPJU/Z7zZU/YvitmT6X1W7zzamF/HNfDZGkqnMXDD820+civ18TA6xoUCbXTSrgma2fWpN0Vs4rtngH+dleZnwu3zr346GEJS5y9cWDcTkEobjC63uqWL+7kkdvmkQ40dQetOwvHxKKKdYgPzM3/P2nSzkR6byv31En4zpgNrqoB1LKY+hpsnMOpmaBGbQEbc2CJud0731X6c2Chu952ciBKWTqeZcWEoor3PPSbovEEowrxJSO+732oK8hmuTBueOty/Wyv3zI/a9+xGUjB7L23cOMHZLDU5sPUjggwLH6qEW2AQjGklZed817h6mPJOkf8FipLrs8/tUXD05RHb/9iiIWri3j5e16oLfhgyqm3PcGfreT/gG3NYVnSI7fGuTXgqvQE+kwGxJSSomhIGNIKZ1TsA9cttf3TVnOJVeOYumsz1kryq2rSzkRjltBkvnPXvb1i/C7XV2uIpkrf16Wl8E5fpZv2IuqaZaxrN9dSZbPbV0ZHnpjHydCcfweBwgsvzXT6yISVxmZl2Gxtz7309epaoixYU+lZahmmddeRTN/vOYPxcwYRJMqkYTK2vcOs3zueMJxpe0Bfj1UgDDxvBDiMXQhuluAN4DHO3XEXgh9dYtT3RgnmlR5cO54IokkK0qKqaiL8sGxer59+fAWK8rCNWW4HKS0s1w3sSAl0t+89Ar2LJvJH2+5FJAdvnSaQZ9pTFWNcX72l4/wuR38bv5kPrn/KiKJphab2ROG4HE5efadw8STCpleF7+6/mJiSY2EohJJqlYl7dWFXyAv00vJpYUtgruHNn5i5WObk3NmPvwPbvr9u7gdgvx+elosFNc7MuyrvIlLhuUSiXcyu9JRZxj4CvAgsBz4yim8zgnsAP5qPB+Onk7bBzwHeIztXuN5ufH3Ybb3uMfYvheYebJjdndwFowmZcljW6ViBB2L1myXDVE9AIrEk7KqISpVTQ9GzADtjme3y48rG2UwmpDbPz0hqxqisroxKlVVk/VWEBW3ApqR97wqSx7bau1zMjRGE1Yg9eSmA7IxmpB1RhBkvt+KjXtlQyQh1+86Kj9riEpFUeWnx/W/L1qzXR6sCUlFVa3Aa9Ga7fLT42Er8Fyxca+sqA3L6saoFWzd8ex2WVEblsFoUiqqKqsaU8+/PhyX1Y1RKwA0X1fdGE05t5LHtsojtWEZjiebf7SO2dVJd9AN742OvmErr18CPGsz3OeBEuPxb4HbjMf/BvzWeFwCPGc8vgjYaRj2cGA/4GzvmN1tuKZRHj6hZxFWbNwrVU2TeyrqZIMReZvGbRrtp8f1TIL52j0VdVJRVVkfTsikospgLGlF7aaRK6omD9aEWvtntoCiqpaxVTdEZX0k0WqGY/unJ6xsg3muzTMg4Vgy5YfwcWVjymepbojKunDcMmi78dWF4zIYbcqOmN+D+R2Y72m+j/5j0T9nfTje2o+0e7IKUkoViAgh+p3iYo4QogC4Bvi98VwAM9AF8KClPq6pm/sn4MvG/tcCa6WUcSnlQfSVd+qpnktXEImrLJxRhM/loH+Gm5unDycSVygckGEl3huiCctvNNlX04vyqGqI8ci8YgoHZBCMKazaclDPYUp9qJ15uTd943VlR1FUedLycDShUdUQY/GVowkbbTLNMxyzJwxh+MBMMgwid2YbjYtet4MMj8vyZ+3VMjMA+88/f8CCacNbkMFve2Y7CHAYpHWzfHz7FUUMyfExMNPDynk6c+z+9R8BugLkwEwvWT53pytnHdVViAG7hRAbMTILAFLKhSd53cPAj2jKQAwA6qWUivHcrnVr6eBKKRUhRIOx//nAO7b3bFUftyfhELBg+nBWbT7I3EuGkp/loyakl1VN8si+qiDFhf15uKSYvCydK5DhdeIQMPmCXDK8LqSEfgE3qibJ8DoJxlKH2i37+kVcdfFgvv90abs6W7oxS7J8LvKN9zWn35gTdZbOGoPH5SDL57aIMPuMqlZz1fAV84oZmJHa2mOqO5pGrGiS//fN4pOq0sSSKnfNHGPxbhfOKOI7XxjB4/On6NMr4ypIyPR1TdKjo8HZq8BPgX+gS9mbtzYhhPgaUC2ltO/XntZtl/Rxe0rYWdP01S/L5+K6SQW88P4RgnGFvEwvobhChsfJb781iYuG9GP1lkP43U7qwwkGZnqIJFQUTbL2vcOEYvo8hTkTC8jwugjFVLbsr7FWuWVfv4jrJxWwuJkGgb08bGY2YkmVuKLRGNMrY9WNegbA3i+WVCUL15RZ/WXmhJzWVk2zhSbg0cXyNuypbDUAM+VK7WjO8NI0Wgz9u2XVNop//jf+5+/70KS0eLpd4mt01KcA/MCYU9j/V+ir4yHgM3QFxj8CxwGXsc9lwAbj8QbgMuOxy9hPoAdm99je19qvrVt3+rjhmF75aogkpKLqwU1tOC4bIgnZENGDmvpwk6+qqKpsiCRkYzQhVU2TqqbJjysb5fpdR2VDJCFVVX9u3h+rixhVrqTls5r+7seVjXLRmu36+6iarDeCr6DNH33ro8+soGz9rqOyMZpICYrs/vZnDXrgZ263H0dR9SqWquq+qqKqsjGq++NVjdE2g8kqI+gzYfr0dzy7PcWHN4O81EA01mkft6NG+HX0iP6g8bwYWNfhg+jCzGZw9gKpwdm/GY9vJzU4e954PJbU4OwApyk4U1VNBqNJuWLjXusfqQdiCStAMw1u5D2vyo8rG1MM9vCJsKwLxy0jUlXNCnLqwwmrVNoQSchgNCk/a2iKvM2oX9U02RjV/24aq6o1lVTNH82Tmw7omQMj62Ee5+PKRplU9MyBouifwZ41aK8Eay9v14XjVmBqf09V1c/PNMBgTP++qo1Mi5mpOFlp3IbuCc4MLEMPiOqNVbrMMKLOYCmwRAhRju7DPmFsfwIYYGxfgiG7L6X8AD0T8SHwOnC71APGHkckqRLwOrlhcgHhuGoFYgGvXlo9WhclnFAsgkpRnh6sReIqJ0JxXt5egUM05XGP1kfJ9LiYP20YGpJwXOFfRg8iw+sk4HVabeJ5WV7unvk57nlpN6Pv1YsZAa/TCr4icZUjtbrQRpZfP6cvX5jP4rVlfNYQ5a6ZYyzNrg17KtlfEybL5yacUPG7nNw8fTiv7Khg3tRCS6SjNpwg2Aaz7aE39pHlczM0N2BRFo/Vx/QysFVs0cu3Abf+/uGETvwxffiTlcZPFR01XEVK2dBsW4cdFCnl21LKrxmPD0gpp0pd63aulDJubI8Zz4uMvx+wvf5+qevmjpFSvtbR43YVAY+TSFyhf8BDQlWtqpkZxDy4YS8OwOUQPD5/MpGkSlVDjHBCwe9x8a+fv4BoUsPrFDx60yT8HgdxVSdgP73lEFJKhub6iSRUgjHFahM3B4nkZXl598czeGLBFMLxpvp/QzRBps/Jd74wglBcsdpx3j9Ui2bwb812GruWwfefLqU2miDT5+KGyQXEVS2l7BxqVna2S/KXV4es45vFiNZ8cYdDWCo5D7/xieXDtzYDrcc6IGzYI4S4EXAKIUYJIR4BtnTqiL0IkYTe5KdJuOPZMrZ/WsuKecUkFL16ZqZ4kqq0+q8eeP1j/G4HfreDWFJj8doyxi77G9GEyh3PliEl3Pm8Pt1GHzGqr+TmJJxLhuVaWra/uHYsToeD2kiCJzcdJMPj5MG543mxtAK3wwESNu+r4ebpw61SrGn8RYMy29QyCMd18Y62ys5N5J2mapc5refBuePbJZdrmrR+ZFWNcSvr8pu3ylt0QHRliElHDfcOdF8zjl5MaAAWdeqIvQg+pwMQVon2wsH9OFoXweNyWCXT5XMnkOVzkWGkeqoa48QVjXBCTSkBD8r26TxcI885Kl//52d49OF3objClvIaHi4ptphcEogmVKuD4dXdleQGPHz78uE4Hfp53bGmjEyv3o7zwA3jLReiLUVFq3vB23rzot/jsMg7P3llt5WbXr+7kjXvHaZ/wNMmuTyWVDkRTv2RmS07NcE4b+2t4tGbJvHJ/Vfxu/l631pPd0BcI6W8V0p5iXH7CTpbrM/CnMSY4XVxtC7KI/OKGZjlYcTALI6HEmzdf5yBmR7qIgmqGmJEEirhhMKDc8fjd7vI9KQm++NGfjOS0O/NFTKSVNm0r4Ysn8tqFw94nBQOCOh5WoOCOHvCEK65eDC1kYTh87qIGCv0vuoQVY1xlv9tL26n4Obpw1m1pXUS9yXDcokmtRZqi+bfIommjo1Xyo7xf1/fy6+uv5i9983i25cPx+9x4nSIFBVyc/XUNCyf2D737Pz+Pp5YMIWrLx5s0SNvXV1KbeTUqZwmOmq493RwW5+BabTH6qO8vL2C6aPyCBkqMQX9/YwdkkMwprCqQkrQAAAeBklEQVTm3cPGiuvi9T2VZBjVo322Fhavy2HpCmDcm2SVTJ+LO9aU0RhVeGVHBTPHDSaSUKkPJ4gYAY7pV4aN1ddUBg8nVP7nxonkBNxWdepfHnybTG8TL/fhkpYGFnA7CXicLS/dJcVWAGpi3c5jPPzGJ9SGE5Y8/nef2obH6eDx+bp64+MLpjAgw5Oyiq/beYzpD7xF8c//hhACDSweb3e0MJ1MV+Eq9Ek45wshVtr+lA0orb+qbyDgcVITjONxObh+cgEZHidCCI7WRcnyufjgWD0zxw1m5rjBKJokElf48oX5PL3lEDdPH86GPZV85/IR1EcT3PPSbp753qVWRuL9Q7VcffFgcgIuq0PWHIR39wu7ePq7U5GGz/zTV/bwwA3jOb+/H8BafRHgc+stMIvXljFrXD6P3jSJLJ+LsHEpX/aXD9l+uJ5ls8dSNCiTSEIhw6MPzgvFVKt50ewDs887s89EW3zlaMvoALYeOMEPntnO4wumWKVewPLR7a81V/Hms9fMz9JTWYVj6AOgY6RWzNahz9jtswgbVbG8LC/LN+y1Vj+9jOpielGepcuV7XcDejSf7XcjBJRMLbS2bT1wwmoiDMX0LMBdXx2DRLBpXw2Xj8rjtme2839f38uy2WOJJTWLJ2u6AMFY0vJff3HtWBRVQyBYtEZXHb9iTD6rtxzis4Y4T246aK2m63dXsmzdB9SGE5bRgq56c/3kpozDsnUfWPPOTH6xyWewk8dN1+eZ712q953ZLvXtTZE8I7RGwG173B8Y39FE8Zm4dUcBwmRfHT4RtpL+ZhWrIaIXGZKKzvay0wKD0aYeM3sVqaI2LEOxpKwJRi1ao/n35pTIJzfpRYkTwZjVq1VRG5b14bgMxpIWy8ssQphsrppgLIXVZRYXzAKBWRUzCyTNq3TmZzWLGWaVy87was4Oa179sh/D3ktn9t51F62xoz7uRiFEthAiF72K9aQQok/LL0WTGsGYLmSxsqTY0iOoMXq7InGVmmCcLftr8DgdhAzCDMBLpRWWD2yuri+WViCNtFqm12UUNfRLq9lyA03KMYqq4XE7yfK5KJlayIulFSQMVpmpOGPmRosGZTJrXD65GZ4UH3Pmw/9gzE9eI8O4lNv1HUzyuSnyMfPhf/Dy9gpWzCvmS2MGpXAmHtr4CQ/OHc+Sr4xuM39rwuHQXQfThTBXeJ/byfINe60uiWWzx7J8w158PZwO6yelbASuB56UUk4GruzUEXsJAm4nTuOSrxgVoZUlxTidui8XTihk+d1cXJBDQzRp9XSZ7dpLX9xFhsfJguk6qWXmuMFW8FJeHaKf34OiSVaUFLN1/3Gr5cZM7vcLePC7nTRG9ZkMZk+Y3+OwLrtmbvRIbYRri89v93JszxbYjdF+WZ936QUMCHhSXAPQfwTLN+xtsR067qdGEmqLH0pVY7zHCxAuY2D0N4C/dupIvQwOhyDgcZFtpKROhBOWWMZ2I/8a8OiriNmmctSYbDPKyJ/urwlbnNtR+ZkEo4plcAGvk2yfm9f2VHLZyIHkZnisSZCmcR+pjbQoKITiTWm3mmCchzbupV/ATbbfncIJNo3xkRuLcQha5G1NY2yeGXA6HSlTdkxUNcbbTaGdDN09Rb2jhvtzdFZWuZTyfSHECPTWmz4Nh0OfURAzUmNmTvXmp7YRjuvl3REDM6x86YMb9hKwVcB+81Z5St42w+O05DhrgnGO1EZ4fU8VxT/fSNG9r/FiaYX1Xlv3Hycn4LZKusGYHghmel38av1HeJ0OqwAi0IPJF0srrO1777uKh0uK0SRUB+MtpEJBN0Y7Cdy8rLdnZJ01PvsUdfsPpdPzfE934HQ6bt3KDoslZTCqM7nM4OWCpX9NEdhI2nq5PmuIprS4mEHOHc82CXbY2VV2oQw7c+tEMCZrglHZaAh6mIwye6D0cWWjDBsBX9jofbMzvuwUR5Ol1dH+traCrLa2d/S77MDruh6cCSF+ZNw/IoRY2fzWuZ9K74DZ2XvLqm34PfqYe7N8ueTKUQRjSUuEY39NmFd2VLB87njysrwcOB4mw6t32+Zlea35CmZhYObD/6Do3tf4zz9/gN/tYP60YXhdDjKMvwdjCTwuJ3c8W8ax+hiL1pbx0Bv72HG4loGZHmvVvmblP9EkhGIqXpeT1/ZUMn/aMN0tiSWtiZDltsqaGRz96vqLybCtss3RVpDV2vaTqVH2hPDfyVyFj4z7baTmcU/aAdHbEUkoVtLd7CKYM7GAD47V882phazecsgKjMy/JVVJTTDOT752IdGkZg2BNi/R+5oxpNbtPIai6cHeR5UNVjFiaG6G5ZPa+QaDcwIEY4qlV1D2n1+16I7l1SEaIvqPSUrdDahqjKUEcaax3/T7d8nwuvC5uq5f1hGj7Anhv5Pp4/7FuF/V2q3TR+0FCNhKn795q9ySybxs5EAWGyugyQ2Yd2khr+yooHBAgISiEYqpFq92YKbbGmT36NvlLYKnDI8uDjKtKA+/y8nlo/IsfoRZsLALcAzK9nHgeBhVg3/uqyYUU6hujHGgJpgif79s3QeWnJIZxP3q+ot1/3J+F/1LGzpilN1dNYMONksKIf5CS/5tA/pK/JiUMtbpMzhLYaaWzEF1Q3J8zJ82zBo1umHxF61S6TsfVfGdy0cQiaucn+NHos+1vfdqfeV9qVR3I/r5Pfg9uguR4dWDPp9b/6dm+fQJOdl+N0frdH6E2aC5fO4E7nphp9UQufjK0byyQ1cX31xew2UjBzKtKI8fPF2aUpa97ZntPHHzFB5fMEXnFie6p1HRjo4YpZmlaK0U3NkJ6h3NKhwAQujqNY8DjUAVMJo+qGijaRKHgEduLOY/v66TZIQQxBWNmJElsK9sX74wH4cD/G4HIYOLunTW51A0nVBTXhPG43RwPBQHBCdCCYLRJJqGpRQTiatk+9xE4gp+j86PyPK6OHA8jMcpeLikmKH9/fQPuK3p5eZYqNue2d5ixBPoBuRzO0/ZJz0VtJY6a54i6+5UGHTccCdKKW+UUv7FuN0ETJVS3g5M6vTRz1LEFBUNid/tIpbUuwTufL6MpKrREE22ScCOKhpPbT5I/4CbQdleK322dNYYkppM6TYIJ/S2oAde/5j/uXEiQugTc6JJlWffOaynuBIKS2d9jjXvHSae1Pjuqm3817oPLMFks+38/UO1HerAhe4PlDpilPZU2N77ZvG7+ZMZkOkhklR7nNaYJ4QoNJ8YjwcaTxOtv+TsR/OVR1U1YwWC48EE0miDycvysmz2WO5+YVerQ5pNAnbA42Tlm+U0xhSCMcXKnfbzu7nz+VRdsTuf30k4rjBioK4fGE0qCCS5GR7mTCzgxdIKNGP+rr2T4ZWyY/z0lT3WOCiTtONzOVr4zytbmbHQ3YFSR/OzDocg4HZSG05a9Miu/Gg66mDcCWwSQuxHbxkfDvybodrYK4M0c+VZuGaHJVxRcmkhpYdqmTluMEM9AYTQfdUlXxlDls+dIitq99cWzijiRDhh+cVDcnQK4rJ1H/DADeNTAj0T+dlenEInfdeGE9bgvPuuG8fSF3fpo5cSKnWRZItOhnU7j/Gza8dy/eQCtuyvYcH04fzAGJBn0hSPh+I6D8DQMAi4nUY1sPsDJTNFBrTwWU1CfsDjtGYAm8J65oDrDK+z+ZC+kx+zIztJKdcDo4DFxm2MlPJVKWVYSvnwKR3xLEHzlee6SQWsffewJZt0pDZiSRwtfXFXC1lR+8q2YNpwFq0ps+aBHamNWD1Xy/+2twWHYPaEIdw1cwzfXbWNDJt00rqdx6zca9GgTItmaGYWTHXHD382k35+N8s37GVEXhZZBunGJNb88Lky4kY6rvnK1hGftLvQ3C0JeFpKTt3z0m5LS/hU0FFF8gBwN/DvUm9NH2oo1fRaNF95huT4mDOxgEyf3vCY5dM7GUxiiZkLraiLpkyPWTZ7rGU4l40cyNr3DuN2CvoH3BafoDmHYMlXRlsuiBnMmcZk5+2afNwdh2t59KZJ/OfXL0TSVMI1SSv2XDHQbhduTwRKbaH54tBcKNoux3qqrkpHfdwn0X3Zy4znFcB9p3SkswzNV8FIQteHDUZ1+uJfdh4jntQ4EdJnfq3beSwlb2vPKli6CsaEmekPvMVP//wBAnh8/hTO6+cjx++2GgULBwTIz/by02suJKmqZPqclmE/+na5ngbbchDQMxvjCvrhEHrXwkuler7YJHy3lh9ur0my2zkD7aD54JZM20LQ2rmdCjo0hFoIsU1KOUUIsUNKOdHYtlNKOaGd1wxFHyl1HqABv5NSrjA4vc+hz0k7BHxDSllnKDOuQG8VigA3Sym3G++1APiJ8db3naz40ZEh1JGEQm24aSjyJ/dfxZLnyvjZ7LHEjeFz97y0m+VzxyMhZXjyIzcW43U5dfGPhIrf5aA2kiRsyOPb/d/LRgzgiZt1XYSFa8rIz/bys2vHURfWY1rTt106awz9/B5LoXv0va/xk2su5PpJOm83P9uHJiVVjXEcApY8v9PKMY/MyyCWVDE7ksNxhVtXl7Y4j8cXTOl03rQzaP4dXzIsl8e+NZnvP93uuXXrEOqEEMJPk5T+SPRW9fagAHdKKS8EPg/cLoS4CF2h5u9SylHA343nAFeh+9GjgFuBR41j5QL/BVyKrqbzX0KI/h087zbRnNhsKif+4JntrN/dJCF/Xj9/CwL0fX/9SK/zG7lRp9PBgAwPg7K97XS/6uXj275UxCqDu2v3bac/8Bbjlm1g9L2vWVeDZX/5UF+1+vlojCUJJ1R2VdThM1q/a4Jx3jlwnNpwgltWl1L887/xr4+/i6bJVs+jJ9yB9mAXwDPdgqc2H7QqiV05t5P+/IyV8Lfo8kdDhRB/BKYDN7f3OillJVBpPA4KIT5Clwe9Fl1LDPSMxNvoskzXAqulfgl4RwiRY3CAvwRslFLWGuezEZgFrDmFz9kCdmIz6AHTwyW6jGZelpfrJjXNerDvB/oK0bzqY/J3fS5nSqUqYET29ik317xZzjcvKSSabL2i5HDAynkTWbhmB6G43pOa6XUhBEwbmccPntEzCMvnjifL505ZwcxGxuYVMzOrcDrRmnbDyjfLuX1GUZfPrSPCzhJd/ON6dGNdA0yRUr7d0YMIIYYBE9El8vMNozaNe5Cxm6WPa8DUwW1re5egByn6L39O8RDuveZCKydq+p4Pzh2fIrnZkRWiNfaUGcnPnjCEYCzJwhlFOAQpvq099+pzOS0/NNvvJtPrImx0MWTaMgghm56ZHW1VzE43WstgLJxRZHX9duUH1VGH5x1ghJTy1VM9gBAiE3gRWCylbNQX8NZ3bWXbKenjorsYFBYWtngB6OmZmKKiaeD3OHA7HTxcUozP5QAh2Fxe0yKvavIUHr1pEtl+d6e+7IDbyW9vmkTQGAa9YNpwa9VcOmuMJXqsdxY3lWVBDyKPh+LUReJMGNrfyiCYost2EWYTXeUBdBfMDEZKrnxqIbeubl+4uiPoaHD2ITov4VN0RXKBvhiPP8nr3OitPhuklA8Z2/YCX5JSVhquwNtSyjHGVJ+3pZRr7PuZNynl943tKfu1htaCM02TBGNJgnGFu1/QE/zL1n3AynnFJFXJef18VNZHGZzjR0oY85PXUGy5RZdD8Mn9V+Fo+4fXLkIxhVtWb2PrgRPs/+XV7b6/vTiiB3NjiSc1hIA17x5m3tRCwgmVgZle/rDpgKUwnp/tZfGVoykcECASV60swpmEqmqWuEoHg8YOnXBHf5JXndrpWr7xE8BHptEaWAcsAH5t3P/Ztv3fhRBr0QOxBsO4NwC/tAVkX6UTKjqRpF6FMqP+okGZ5Gd78budbDtUQ7ZvEP38Hg6fiFiS9C1WMUPJpjMwNcM2LP4i0XbYUgF3U4XJ/PudXx3Dj/60i5lj85k/bZiV8cjP9nLXzDEW+8zjcrBwTVmXV7PugqZJaiNJa8Xde99V3Va162jl7NPWbid52XTgW8AMIUSZcbsa3WC/IoTYhz6C6tfG/uvRWWjl6IyzfzOOXQv8AnjfuP3cDNROBQFPk77ssq9fRDiu8Is54wh4dXEPiT4F8uE3PiHb727hez44dzxd+f+bsxGWrfsgRUzO7jf7XQ69c6GZ3zokR1dv/MpF5xFNqBbvwdT2ml18Pv38nm6VOOoOtFaA6K6qXY85QVLKTbS97H+5lf0luip5a+/1B+APXTmfSELleDDOwhlFXD+pgKhRP4/EFTJ9LoJRfWrkiIEZ+D1O7n15b4o80fINe3nom8WdPr49NQSgSfjV9Rfrl3VjpTWvCnWRVD5EeXWIpbM+x5Lnd1pSTibW7TzG+t2VfHJ/961m3YXm1Umz+mgfnNLZNF1H87i9Gqqq4RKQE3DznS+MINPnYmCm1xDKEETiKll+FwGP0xgF1ZQq++FzZQD89zeKO1VTN9Fae/iVD/0vgBX1m1cFe1XM5RBs2FNpUSTbWrW60jreU2ieVVi38xgfHKu3Jl92RWq0zxuuqmqcCCf4rDHO1v3HQWLxA8JxhYDXiTAYVCfCCcOHlbpyy5WjUgghdsn4U0VHyC2RhEp1Y6xFY+Nsm9hHc4HkJVeO4rFvTSbgcXZLYr874Xc5Us5pyZWjmHxBLk9uOsi+qhABj4twQkVVT2GIsYEOZRV6G+xZhWAsyZObDnL7jCJqQwkGZHmRUlIbStA/w0MsqbKvOsiF52WTUCUOIfC5HdSFE7hdDm57Znu3lE6b0yhbC540TRJNqtRFUsukD84dT27AQ62x3cweDM31cyKcYJERkC2cUcTN04frElFnqOjQ/POuefdTZo4bbKlFbtpXw6QLclm8timINOesGefaoRPu84arahqhmILDobfMDMz0AII/bDrAd78wAgEohvz7oCwvcUUjqUlWbT7Iv395FKPv7b60mJ2b2pZhaVKy5LkybvtSkeVfP/p2Of/9jQmEYgp1kSRDcwMcqY2Qm+E5Wd3/jCEUV7hl1baUcyu//yrCcZUfPNN1rsKZ/XSnAZGEigQyPC4CuS7iRpR9/eQCVE3qX5bQtWiDsSQS8DodlEwtbFUrtivJ/fYI1/bzba3EHE1qZPncOJ0OfaRolrdHSOHdhdbOLRRXLEK+HZ055z7v42Z4XWT73eyrDhGMJQG97NY/4CbT52Jfdcgav5TtcxOKKTREkxav9nSTVdrjy1qGb1wA2hS5O4MBmYnWfPpsn7vDvXEnQ593FUKxJBrwlOHnRhO6T3t+ToCAx8UfNh3gO5ePIKYoeJxOS5LTrGzNnjCE26/QL9vRxOmpRrXnUjSvqtnn5p4NRQf7Z2ju0/9+wRSiSYVYUks557SPa8BuuJGEghNoiCm4HIKcDA+N0SSrtxziu18YQV0kwe6Kej4/ciBupwNVk9QZXIWzyXc0jRmJVToGndW25CujU/LBZ9poTTT/ASIlt6wutTggZs/ZoCwvgVMs+fZ5V8HndpLU4Gh9BLfLQVVDjGy/m+smFfDEPw/gdzu5cHA/srx6HveVHRX0C7SsnLXWMXu6YO/d8ntOng8+k7B3TputQiZDzWwaNXvjRv54PVc+9L/4OuGTnxPBmd/joCAnQIbXRemntWT6XJzf38/KN8sprwlz+xVFCCFojCZ5fU8VpZ/Wt8naOh1ouVJhlU7PZjbYyVJ+3alo0+dX3IDbSTyp6aNI4woXDe5HTTBGJK6ycEaRdckqrw7pyuC2sUu3rN7GiVCiy0Z7KsoxrQl22KtunZ3Q2J3qNW3hZJoN3dmo2edXXNB5Absr6rl8VJ413xYkJVMLWWRPhJcU08/r6tbOgY4UHuyw//NB72gwGza3HjjBup3HgJY8h/bO8VTPobM4WXrO3qjZ1e+3z6+45gT0LxgqiKYqTF0kyaK1qWyqRWvLSGp0a+fAqSrHNP/nz54wBL/bleJz1wTjuJyCWFLt0Dn2hMxnq8c5jZoNfd5w/W59Ig4Iay7Z+4dqrdkKdugD9Lo3ADvVIkHzf/7tVxTh93RtYs3pKlSczBXoTt2yPm+4kYTKU5sPEvA6+XPZUYtF1RFuaHf4hae6Ctn/+XOKhzAqPzOlYbMzE2tO10p4Ms2G7lz5+7ThqqpmydM3RPSMwfZPa1kxr7hFE6SdZWUK4HXH6tDRgMT8kejlZydPLJjCvddcyOETkVYbNlecQnrudKrXtCXBDy0FQjYs/qI+Ub4TK3+fLkAEY0lOhBKWsIcQgjuf35nCrjI7Tu0sqw4KV3QYJyPXtBY8mcfPy9K1tswB1UWDMq1xrU5nx9edjhB8ehqtCYQ8OHc8uRkeu+hdmmRjH+J8Xj8/dz5fZimDB7xOjtZFGZLjI5rQWNRsyHJbbd+d6o86CbmmtUyCeXyTmWYq3QhjvFM73dKdOofTgeZdIKa28OPzp5zye/VpVyFsjDE1hziPGJhhKYNLCaomdf5BK8IV3dkfdTK0Fjw1P76q6aXe0fe+pueXuzi15kzA3jCa4ip0IiDu04YbcDcNxFu27gO+84URxFUtRRlclbQ6vG7DnsrT1lHQWvBkP3576ou9CfaGUVMw8K6ZYwzds1NDn/ZxIbWvPxJXUwgqAPt/eTV3Pl/Gkq+MadbEV0xuwENU0XrcL2yrQJAbcFvH705C+5mCXVvCxGUjBvD4/Cn2tv+0jwvgdDrIMoKYtlwCe49Xk0q2LmaXaby2J/3C9ipKmU6HJWN6NvITTgWtff+dzZ33aVehOVojXpuX5J4YXncqaDeNdBrTWT2J7swn93lXwQ5zzGmq2svpcwm6grMhndVVdJAz0beI5EKIWejCz07g91LKX7e1b3vCzn3BAM5mdCRnfZLvv+/4uEIIJ/AbdMmmCuB9IcQ6KeWHp/peZ0M+s6+iIytqd33/vcXHnQqUSykPSCkTwFp0Ieg0ziKcLhYa9B7D7RFx5zS6F6ezXb63GO5JxZ2FELcKIbYJIbbV1NScptNKw440H7clKoChtucFwDH7DlLK30kpp0gpp+Tl5Z3Wk0tDx+lM2/WW6OR9YJQQYjhwFCgBbjyzp5RGc3Rna87J0CsMV0qpCCH+HdiAng77g5TygzN8Wmm0gtOVtekVhgvWPOH1Z/o80jg70GsKEKcCIUQN+qAVOwYCx8/A6ZzNOBu/k+NSylkn26lPGm5rMMe6nunzOJvQm7+T3pJVSCONFKQNN41eiXPJcH93pk/gLESv/U7OGR83jb6Fc2nFTaMP4ZwwXCHELCHEXiFEuRDiP870+ZwuCCEOCSF2G1M9txnbcoUQG4UQ+4z7/sZ2IYRYaXxHu4QQk87s2bePPm+4Ni7vVcBFwDwhxEVn9qxOK66QUhbb0l7/AfxdSjkK+LvxHPTvZ5RxuxV49LSf6SmgzxsuaS5vc1wLrDIerwLm2LavljreAXKM6fZnJc4Fwz2XubwS+JsQolQIcauxLV9KWQlg3A8ytveq76nXcBW6gJNyefswpkspjwkhBgEbhRAft7Nvr/qezoUV96Rc3r4KKeUx474aeBndbaoyXQDjvtrYvVd9T+eC4VpcXiGEB53Lu+4Mn1OPQwiRIYTIMh8DXwX2oH/2BcZuC4A/G4/XAfON7MLngQbTpTgb0eddhXOYy5sPvGyoOrqAZ6WUrwsh3geeF0J8FzgMzDX2Xw9cDZQDEeDbp/+UO4505SyNXolzwVVIow8ibbhp9EqkDTeNXom04abRK5E23DR6JdKGe5ohhNjSydfN6Qo5SAgxTAjRZ7Qo0oZ7miGlnNbJl85BZ7d1FsPoQyIq6TzuaYYQIiSlzBRCfAlYht4ePg4oBW6SUkohxK+B2YAC/A14Cfgr0GDcbgBmoNMPPehFg29JKSNCiKeARmAKcB7wIynln4QQ7wAXAgeBVVLK/3d6PnEPQUqZvp3GGxAy7r+EboQF6Fe+rcDlQC6wl6ZFJce4fwr4P7b3GWB7fB9wh22/F4z3vAid0mke769n+vN31y3tKpxZvCelrJBSakAZ+uW8EYgBvxdCXI9efm0N44QQ/xRC7Ab+FRhr+9srUkpN6sLX+T13+mcOacM9s4jbHquAS0qpoLO4XkT3a19v47VPAf8upbwY+Bnga+N9++ScgD5PsultEEJkAgEp5XrDLy03/hQEsmy7ZgGVQgg3+op79CRv3fz1vRrpFffsQxbwVyHELuB/gR8a29cCdwshdgghRgI/Bd4FNgLtEcRN7AIUIcROIcQPT7r3WY50ViGNXon0iptGr0TacNPolUgbbhq9EmnDTaNXIm24afRKpA03jV6JtOGm0SuRNtw0eiX+P9l76hs5YgqvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c949b3978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c95a830f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c95ab8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c95b21f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c95b584a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c95bc6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c95bff588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c95c4f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c95fea860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c9606d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c960b4ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c960e7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c9614fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c961b0358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c961e04a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c962316d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.figure_format='png'\n",
    "for v,i,j in corr_list:\n",
    "    sns.pairplot(df,x_vars=col[i],y_vars=col[j])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与年份有很大关系，yr=1的类普遍比yr=0的类数量多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征与类别间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c962f31d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IeD169ACguF61oDX8Qaj020Pft1ign5yIiMS0iooKpyMkvN69e+NyudhbGzu7EGPZvvD3KScnx+Ekn1OhExGRmHbwgH2AxsbYmVYjkSQlJdG/Xy7bq3SuZGtsC3+f8vLyHE7yORU6ERGJac2nhjhw4ICDSRLbiJGj2FrlIxQ7Fz+IWVsqPGSkpZKdne10lENU6EREJKYVFha2+LW0rzFjxlDTaDXfYytsqPAxZuw4jImdk0hU6EREJKYVbNlCKKULAFu3bnU4TeIaN24cAOtKkxxOEtuK6lwcqDWHvl+xQoVORERiViAQYPPmzQQ6ZUNyJ9avX+90pISVlZVF/365rCn1Oh0lpq0JF96JEyc6nOSLVOhERCRmrV+/nsaGBoLpPWlM68Gq1asJhXQ1g0g5fvIJbKpIoj7gdJLY9WmJl149ssjNzXU6yheo0ImISMxavnw5GBeBTr0JdM6muqqKdevWOR0rYU2ePJlgCNaVabdrSxqCsL7My+QpU2Pq+DlQoRMRkRgVDAZZ9sabBDr1AY+PQJdcjMvDG2+84XS0hDV69GhSU5JZXazdri3ZWJ6EP9hUfGONCp2IiMSk999/n5LiIhq7D2la4E6ioWt/Xnv9dV09IkI8Hg+Tjp/M6tJkTV/SglXFXnw+b8ydEAEOFTpjTBdjzLPGmI3GmA3GmBOMMd2MMcuMMZvDn7uGH2uMMfcaYwqMMf8xxoxv9jpzw4/fbIyZ68R7ERGR9met5dHHHoPkDAJdPj9WqaHnCOrr6njuueccTJfYpkyZQoX/88lzpYm1sLo0mfz8iTF1DdeDnBqhWwAstdYOA44DNgA/B9601uYBb4ZvA3wTyAt/XAksAjDGdANuBo4HJgE3HyyBIiIS315//XU2f/YZdb3HguvzP1WhtO4Euvbj739/SpMMR8jkyZNxuVx8UqTj6JrbUe2mpA6mTp3qdJQWRb1+G2M6AdOBSwCstQ1AgzFmNjAj/LBHgX8BNwKzgcestRb4IDy61zv82GXW2tLw6y4DZgFPReu9tJeFCxdSUFAQ0XXs3r0bIOKzWg8ePJj58+dHdB0iktiKiopYeN99hNJ7EMgcjG/nBwD4c5uOW6rvO4mkdS9w992/56677sTl0tFD7alTp04cN2YMKzevYs6gOqfjxIxPirwYYzjhhBOcjtIiJ/4VDASKgP82xqwyxvzVGJMG9LTW7gUIf+4Rfnw2sKvZ8wvDyw63/CuMMVcaY1YYY1YUFRW177uJE3V1ddTV6R+mSLzpaNuvhoYGfvvb31JTW09t/xPBGFy1pbhqP7+eq/VlUJczkY8//ognn3zSwbSJ68Tp09lTY9hTo7J80MfFyYwZPZquXWNzZ6ATO8g9wHhgvrX2Q2PMAj7fvdqSls4LtkdY/tWF1j4IPAiQn58fc4d5RmNE67rrrgNgwYIFEV9XIti9ezc1VW5u/6ST01GkmR1VbtLCo80dRaxvv9pTMBjkjjvuZO3atdQNnIFN6XzYxzZmDcNddYCHH36Ynj17MnPmzOgF7QCmT5/Ovffey0cHfJw9wJnBgLqAITk5mbPOOoslS5ZQF3BucrzdNW52V7s4b8YMxzJ8HSeqdyFQaK39MHz7WZoK3v7wrlTCnw80e3zfZs/PAfYcYbmIiMSZQCDArbfexttvv4U/J59A5sAjP8EY6gdMI9ipN3fccQevvvpqdIJ2EN27d2fMmNF8cCAF69B/I2oDhrPOOotrrrmGM888k9qAc/O+fbDfi8sYTjrpJMcyfJ2oj9BZa/cZY3YZY4ZaazcBpwDrwx9zgTvDn18MP2UxcI0x5mmaToCosNbuNca8Btze7ESImcAvovleJHFlZ2fjD+zlpvGVTkeRZm7/pBO+CB8HKtFXUVHBzb/5DatXrcKfk09D7zGte6LLTe3g00gteIO77rqLffv2MXfuXB1T105OOeVU7vnPGnZUu+mfEYz6+lM9liVLlmCt5eWXX6anx5lmaS28fyCFsePGkpmZ6UiG1nDqt34+8KQx5j/AWOB2morcacaYzcBp4dsArwBbgQLgIeAqgPDJEP8FfBz+uOXgCRIiIhIf1q1bxxVX/oDVn/6HugEntr7MHeT2UJt3Go3d83j00Ue56aabKC8vj0zYDubkk08mKcnD/+51ZoqOFI+lvr6e5557jvr6elIcKnSbyj0cqDXMmvVNR9bfWo5MMmOtXQ3kt3DXKS081gJXH+Z1HgEead90IiISaQ0NDTzxxBM8/vjjWF86NUO/SSi9x9c/sSUuN/X9pxFMzeSDjz5m7iWXcMNPf8q0adPaN3QHk5GRwYknTmf5u2/xfwbV4nM7ncgZb+9JJjUlhenTpzsd5Yg0Li0iIlG1YsUK5l4yj8cee4yGboOoGj677WXuIGNo7DmCmuHforzBxa9+9Stuuukm9u3b1z6hO6izzz6b2kZYvi/2JtKNhnK/4aMiH9884wySk5OdjnNEmgZaRESiYtu2bTzwwAN88MEHkNyJ2iGnE+zcvsdEhlK7UT3823j3r+X9Dz/i4+99j+985ztceOGFZGRktOu6OoLRo0czePAgXiss4KQ+flyxdT36iHujsOkSaOecc47TUb6WRuhERCSiCgsLueOOO7j00kv5cMUn+HMmUDXy7HYvc4e4XDT0HkPVyHOp7ZTLU089xfkXXMiTTz5JbW1tZNaZoIwxXHDBheypMaws8jodJ6pqA4Y39qRy4onTycnJcTrO19IInYiIRMTWrVt56qmneOONN8C48fcYQUPv47BJ0dl1ZX3p1A88iYZeowgUruShhx7i7089zf/57hxmz55N586Hn+dOPjdjxgweefivvLgjxISshg4zSvf6rmRqG+Hiiy92OkqrqNCJiEi7sdayatUqnnrqaT7++COM24O/50gaeo3CJqU6kimUmkndkJm4qosI7F3NI488whNPPMmZZ57BnDlz6NOnjyO54oXb7WbepZdx66238tEBL5N7NjgdKeKqGgyvFqZy4rRpDBkyxOk4raJCJyIix6yuro433niD5557nu3bt2G8qfizx9PQYxh4YuNg8lB6FnV5p+GqLaNx/1peePFFXvjnP5lywgmce+65TJgwAWM6yPDTUTr55JP5+5NP8Oy2bUzIKiUpwQ/YWrwjBX/AcNnllzsdpdVU6EREpM127tzJSy+9xMuvvEJtTQ02LZP6/tOarvTgis0/MaHUrtQPOBF/9gSSDmzg/Y9XsXz5cnL69uXs2bM5/fTTdQLFl7hcLn501dXccMMNvL4rmTP71TsdKWL21rh4ozCFb55xBv3793c6TqvF5r82ERGJWY2Njbz33nv888UXWb1qFRgXjV360dh3OMH0nhAno1zWm0pDzgQa+hyHp3QbO4s2ct999/HAgw9y6imn8O1vf5thw4Zp1C5s4sSJTJ58PItXfMjUXn66+BLv0sLWwt8L0vElJ3PZZZc5HeeoqNCJiEir7N69myVLlvDyK69SWVEOyRn4syfQmJXn2PFx7cLlIdA9j0D3PFw1xSQVbWTp68t49dVXGThoELO//W1OPfVU0tLSnE7quKuvvoZ5l6zgH1vS+MGIaqfjtLtVxUl8WpLEj340j27dujkd56io0ImIyGEFAgGWL1/OP198kU9WrmyawLdzXxrzJjZNO2IS62CqUFp3/GnT8PedRFLJFrbs+4x77rmHv/xlEaeddirf+ta3GDp0qNMxHdO3b1/Ov+ACnnjiCWb0qWdol4DTkdpNQxCe3JJBv9y+nHfeeU7HOWoqdCIi8hVFRUUsWbKEFxe/RHlZKfjS8fcZR2PWEKy3A4xUub009hhOY9YwXDXFNBRt5OVXl7JkyRLyhgzh3HPO4eSTT8bn63hXULjooot4/bWlPPZZiFvyy3AnSKd/aUcKRbWGe378Ezye+KtH8ZdYREQiwlrLmjVrePbZ5/j3v98lFAoR6JxDQ954gp1zEm40rlWMIZSehT8969Co3WeFm7jrrru4789/4Vtnnck555xDz549nU4aNSkpKcy/9jp+/etf88buZE7vG/8nSOyvdfHyzlROOeUUxo0b53ScNlGhExHp4AKBAO+88w5P/+MfbP7sM0ySr2kS4Kxh2OROTseLHR4fjT1H0NhjOO6qfTQe2MDT//gHzzzzDCeddBLf/e53GT58uNMpo2LatGlMnJjPC6tXcEJPP5288X2CxN8L0kjy+vjRj37kdJQ2U6ETEemgGhsbee2113js8Sc4sH8fpHShvt8UGjMHg1t/Hg7LGIKdehPs1Bu/vxrvgQ38693lvP3224wbN5558y5hzJgxTqeMKGMM11wzn0svncezW1O5dFiN05HabG1pEquKvVx55Vy6d+/udJw2079YEZEOJhQK8eabb/LgQw9RdOAAofQs6vNOJdi5b9xMORIrrC8df9+J+PuMJaloI6vXrePaa6/luLFjuebqq8nLy3M6YsT069ePs88+h+eff46ZOfXkpAedjnTUQhae2pJO7549+M53vuN0nGPSAQ+IEBHpuDZu3MiPrrqK2267jf01IWqHzKRm2FkEu+SqzB0LdxKNvUZTOfo71Pc9nv+s28gVV17J7373O8rLy51OFzHf+973SE1J4dmt8Tltzfv7veyqcnH5lT/A6/U6HeeYqNCJiHQAgUCAv/3tb1x11VV8tnUndQNOpGb4t8InO6jItRuXh8ZeI6kcdR4NPUfyytKlXHLJPD788EOnk0VEly5d+D/nX8AnxV62V7mdjnNUgiF4cUc6gwYO4Bvf+IbTcY6ZCp2ISIKrra3lZz/7GX/729/wdx1A5chzCHTPU5GLJI8Pf99J1Az/FmUNhhtvvJEnn3wSa+P75IGWnHvuuaSnpbJ4e4rTUY7Kx0Ve9tUY5l4yD5cr/utQq96BMea61iwTEZHWidZ2tb6+nh//+Cd8smoVdf2nUT/wJPDEz9xpvp0f4K4twV1bQsrGV/Dt/MDpSEcllJpJ9fCzaOw2iIceeoiHH37Y6UjtLj09ndlnn8PKYh9FdfFRjKyFpbtS6ZuTzbRp05yO0y5ae1LEXGDBl5Zd0sKyhLJw4UIKCgqcjtEuDr6P665LjB4+ePBg5s+f73QMkWMRle3qwoUL2bRpI3WDTybQtX97vnRUuGpLMcFGADxV+4jL6xK4PNQPnI51uXniiScYMWIEU6ZMcTpVuzr77LN5+umneKMwmQvyap2O87W2VHrYWunm+kvnJMToHHxNoTPGXABcCAwwxixudlcGUBLJYLGgoKCA1Ws3EEyNr+u5tcTV0DTMv3LrfoeTHDt3banTEUTaLJrb1Z07d/Lyyy/T0HNUXJa5hGIM/n6TSaot4i+LFnHCCSdgEmiXd1ZWFlOmTOW9j95lzqBaPDHekd7Z4yPZ52XmzJlOR2k3XzdCtxzYC3QH/tBseRXwn0iFiiXB1G7UDTvD6RjSTMrGV5yOIHIsorZdfeutt8AYGnqPas+XlbZyefD3GEnh9n9TUFCQcFOanHnmmbz77rt8WpLEhKxGp+McVkMQPixKZsapJ5OaGp9n57bkiIXOWrsD2AGcEJ04IiKJLZrb1eLiYkxSCjYpcf5oxbtgSleg6Vq5iVbo8vPz6ZSRzocH/DFd6D4t8VIfgFNPPdXpKO2qVcfQGWPOBe4CegAm/GGttbomjCSsndVubv8k/n/F99c27fvomRpyOMmx21ntJlH+BEZju5qRkYEN+CEUAJfmkY8Frsam48syMjIcTtL+PB4PJ834Bq+/+hINwWq8MTqLycdFXjp3ymDs2LFOR2lXrf0X/jvgW9baDZEMIxIrBg8e7HSEdtMQPiHG1y/+31MeCfWzifh29bjjjuOpp57CU76LQLcBkVqNHAVP2XZ8vmSGDBnidJSImDp1Ki+99BIby5MYkxl7o3SBEKwpTWb6qdPweBLrPzmtfTf7VeakI0mkM2gPntm8YEFCn5QejyK+XZ04cSJ9srPZvXc11V1ywRWjQyYdhKu2lKTSrcyeMwefL36mjjka48aNw+fzsrokNgtdQYWHmkbLCSck3pFkrT0PZYUx5h/GmAuMMece/IhoMhGRxBbx7arb7eba+fMxtWX4Cle250vL0QoGSN3+Lp07d+bCCy90Ok3E+Hw+xo4dx9ph0G2RAAAfAklEQVSy5GN+rdz0ACnuECnuEMO6NJKbfuyT1qwpTcLlcjFhwoRjfq1Y09pC1wmoBWYC3wp/nHUsKzbGuI0xq4wxS8K3BxhjPjTGbA5v5Lzh5b7w7YLw/f2bvcYvwss3GWNOP5Y8IiJR1u7b1ZZMnjyZ2bNn492/Fk/x5vZ+eWkNa0ne9r+Y2lJ+8fOf06VLF6cTRdTEiRPZV2OOeZLhi4fU0i8jSL+MIDeNr+TiIcc+v93aMh8jR4wgLS3tmF8r1rT2u+0CfmytnWetnQf8pB3WfR3QfHfDXcA91to8oAy4LLz8MqDMWjsYuCf8OIwxI4DzgZHALOAvxhjtTxCReBGJ7WqL5s+fz9hx40jZ/h6esh2RWo20xFp8O5aTVLadH/3wh0yePNnpRBGXn58PwLqyJIeTfFFVo2F7pZv8iROdjhIRrS10Y6y15QdvWGvLgHFtXakxJgc4E/hr+LYBTgaeDT/kUeDs8Nezw7cJ339K+PGzgaettX5r7TagAJjU1kwiIlHWrtvVI/F4PNx2660MHTqElK1v4yndHonVyJfZEL4dy/EWbeKiiy7iu9/9rtOJoqJfv35kdu3C2tLYKnQbypKwwPjx452OEhGtHqEzxnQ9eMMY043Wn1DRkj8BPwMOzqOQCZRbaw/uIC8EssNfZwO7AML3V4Qff2h5C88REYl17b1dPaK0tDR+f/fdDB82jJStb5NU9FmkViUAoSDJW9/BW7SJCy+8kMsvvzyhrgxxJMYYJkycxIZyHyHrdJrPrStNIjUlmeHDhzsdJSJaW+j+ACw3xvyXMeYWmmY6/11bVmiMOQs4YK1tfoRuS7/l9mvuO9JzvrzOK40xK4wxK4qKio4qr4hIhLRqu9qe26+MjAz++Ic/MGHCBJK3/xvv7k+arlIu7SvgJ3Xz6ySVbuPKK6/kyiuv7DBl7qAJEyZQ1QC7qmPnSKh15T7GjhuXcNOVHNSqQmetfQw4D9gPFAHnWmsfb+M6pwLfNsZsB56maVfrn4AuxpiD3+UcYE/460KgL0D4/s5AafPlLTzny/kftNbmW2vzs7Ky2hhbRKT9tHa72t7br5SUFO66805mzZqFb89qkre+0zTxsLQLU19JxsaX8dYc4Je//GVCn9F6JAd3a8bKbtcDdS4O1Bry8xPz+Dlo/Qgd1tr11tr7rLULrbXr27pCa+0vrLU51tr+NJ3U8Ja19iLgbeA74YfNBV4Mf704fJvw/W9Za214+fnhs2AH0DTn6EdtzSUiEm3ttV09Wh6PhxtvvJHLL7+cpNKtpG1aimk49jMIOzp35R4yNi4h3RPkj3/8I6eddprTkRyTlZVF/365rCn1Oh0F+LxYJuJ0JQfF0rjjjcDTxphbgVXAw+HlDwOPG2MKaBqZOx/AWrvOGPMMsB4IAFdba4PtGWj37t24ayt0MfgY464tYfdujSiIHAtjDBdffDG5ubncdtvtuDcspnrQyYTSezgd7YuCDSQnJ3PWWWexZMkSqoMNTif6KmtJ2r+e5MKP6Jubyx233052tg7pnnT8ZJ5/dif+IPgc3vO6psRLzx5Z5ObmOhskgo5tkphjZK39l7X2rPDXW621k6y1g621c6y1/vDy+vDtweH7tzZ7/m3W2kHW2qHW2ledeh8iIvFq+vTpLFr0F7K6diJ906skFW1yOtIXmEADZ511Ftdccw1nnnkmJhBjhS4UIHnb/5K860OmTJnC/YsWqcyFTZo0iUAI1js8fUkgBOvLvUw6fnJCH8sYSyN0MSc7O5t9fg91w85wOoo0k7LxFbKzezodQyRhDBw4kL8+9CC/veUWVq54D1dNMf7cyTFxqTDr8bJkyRKstbz88stYT6rTkQ4x/irStryFqS3l0ksv5eKLL8blcnScJKaMGTMGn8/Lf0q8jOvu3GXAPqvwUBeA448/3rEM0aDfPBERoVOnTvzurru46KKL8BZtIm3TKxh/tdOxwO2lvr6e5557jvr6enDHxjFZ7opCMjYsJo167rzjDr7//e+rzH2J1+tlwoR8Pi1NdvRk6k+LvSR53Ak7/9xB+u0TERGg6dqvV1xxBbfccgspgSoyNryEu3Kv07Fii7V493xK6mev0y+7Dw8+8ECHuPpDW02ZMoXiOthd49xo7+rSZI4bO5bU1NgZ3Y0EFToREfmC6dOn8+ADD9CnZyapn71G0v71mq8OINhI8pa38e1eyTe+cTKLFv2FnJwcp1PFtINl95NiZ0ZW99W62FtjmDJlqiPrjyYVOhER+Yp+/frxwP33M/n4SSTv/ADfjvcg1K4TCcQV468mfdMreMt38MMf/pD/9/9+TUpKitOxYl737t0ZOiSPVSU+R9a/Klwkp0yZ4sj6o0mFTkREWpSens7tt98ePq7uM1I3vw4Bv9Oxos5VU0zGxiWkhuq48847Of/88xP6bMn2NnXaiWypcFPuj/737JNiHwMH9KdXr15RX3e0qdCJiMhhuVwurrjiCm666Sa8NQdI3/QKpqHG6VhR4y7fRfqmV8nqksH99y9K+DMlI2HatGnA56Nl0VLZYPiswsOJ00+K6nqdokInIiJfa+bMmfz+978n1fqbSl19pdORIs5TupXUgjcZNKA/ixb9hX79+jkdKS4NGDCA3r16sjLKhW5VsRdrYerUxD9+DlToRESklcaNG8ef/nQP6UmQ/tlSjL/K6UgR4yndRsrWdxg1aiQLFvyJzMxMpyPFLWMMJ04/ifVlXuqieJGfT4q99MjqTl5eXvRW6iAVOhERabWhQ4fyp3vuIc0D6Z+9hmmsdzpSu3NX7iFl6zuMHDmSu3/3O9LS0pyOFPemTZtGIETUru3qD8LaMi/TTpzeYY53VKETEZGjMnjwYH73u7vwBOpI2fIW2JDTkdqNqa8kbcvb9OuXy1133qkzWdvJyJEj6dwpg0+KolPo1pYm0Rj8/Pi9jkCFTkREjtrIkSP52c9uwF21D++eT52O0z5CIVK3vUOqL4k777iD9PR0pxMlDLfbzQlTprK61EcgCv1/ZZGX9LRUxowZE/mVxQgVOhERaZOZM2dy6qmn4tv7Kaa+wuk4xyzpwAZc1UXccMNP6d27t9NxEs7UqVOpbWy6tmokhSx8WprM5BOm4PF0nEvWq9CJiEibXXXVVfh8XnyFnzgd5dgEG0nZ9ynjJ0xgxowZTqdJSBMmTCApyRPx6Uu2VHqoaugYkwk3p0InIiJt1q1bN86ePZuk8h2Yxlqn47SZp3QbtrGeS+bOdTpKwkpNTWXcuHF8Wpoc0fWsLk7C5XIxceLEiK4n1qjQiYjIMTn99NPBhnCXFzodpc085Tvp0bMXo0ePdjpKQps8+QT21Rj21Uaufnxamszo0aPIyMiI2DpikQqdiIgckwEDBpCalo67psjpKG3mrStm3NjjOswUF045eKWNSE1fUuY37KxycfzxkyPy+rFMhU5ERI6JMYZevXriaojTXa42hPXXdojrfTotOzub7D69+U9JUkRef224KE6aNCkirx/LVOhEROSYGeJ/ZEujc9ExcdLxbKyIzPQla0qT6NqlM4MGDWr/F49xKnQiInLMiouLsUmRPdg9YowLk5RMSUmJ00k6hPz8fPwBS0E7T18SsrC+3MeE/Ikdspyr0ImIyDEpKiqioqKcYEpXp6O0WSC5Kxs3bnQ6RocwduxYXMawtqx9d7sWVrup9DcVxo5IhU5ERI7JBx98AECwUx+Hk7RdoFNvNm/erFG6KEhPT2fYsKGsK/O16+uuCxfE8ePHt+vrxgsVOhEROSavvroUUroQiuMRusauAwB4/fXXHU7SMYyfkM+2Sjd1gfbbNbquLIm+Odn06NGj3V4znqjQiYhIm23cuJH169dR3z0P4vi4JZvSmVBGL557/nkCgYDTcRLehAkTCFnYUNY+x9EFQrCpwsuE/I41mXBzKnQiItJmjz3+OMbjozFrqNNRjll9r1EUFxWxbNkyp6MkvBEjRuDzJrG+nY6j21LpwR9oKoodlQqdiIi0yfr161n+3nvU9xwJ7shenzMagp37EkrrzsOPPILf73c6TkLz+XyMHnMca8vb58zodaVJuIxh7Nix7fJ68UiFTkREjpq1lj//5S8YbyoNPUc6Had9GEN9Tj7FRUW88MILTqdJeBMnTmRPtaG0/tiryJoyH8OGDe1wl/tqLuqFzhjT1xjztjFmgzFmnTHmuvDybsaYZcaYzeHPXcPLjTHmXmNMgTHmP8aY8c1ea2748ZuNMbqisohIlLz77rusW7uWut5jwR2ZWf+dEOzUh0DnHB597DHKy8udjpPQJk5sOt5tTemx/f5UNxq2VrrJn9jxrg7RXPvO6tc6AeD/Wms/McZkACuNMcuAS4A3rbV3GmN+DvwcuBH4JpAX/jgeWAQcb4zpBtwM5AM2/DqLrbVl7RnWXVtKysZX2vMlHeGqrwQglNzJ4STHzl1bCvR0OoZIhxUIBFh0/wPY1K40Zg1xOk678/ediGfdP3n88ceZP3++03ES1oABA+ie2Y3/lPg5qU/bd3GvLU3C2s+vE9tRRb3QWWv3AnvDX1cZYzYA2cBsYEb4YY8C/6Kp0M0GHrPWWuADY0wXY0zv8GOXWWtLAcKlcBbwVHtlHTx4cHu9lOMKCqoAGDwwEYpQz4T62YjEm1dffZW9e3ZTl3cqmMQ7cieU0pWGzDz++eKLzJkzR9d4jRBjDJOOn8zby14hEKrG08ZfpU9LkshIT2PYsGHtGzDOODFCd4gxpj8wDvgQ6Bkue1hr9xpjDk4kkw3sava0wvCywy1vN4n0P7PrrrsOgAULFjicRETiWSAQ4PHHnyCU3oNg575Ox4mYhj5j8ZYW8PTTT3P99dc7HSdhnXDCCbzyyitsrvAwvOvRTxcTsrCmLJnjTzwBt9sdgYTxw7H/Whlj0oHngOuttZVHemgLy+wRlre0riuNMSuMMSuKioqOPqyIiENibfv1/vvvc+DAfvy9RkVl3rlQajesOwnrTiKQ0YtQareIrxPA+tJp7DaIV19dSm1tbVTW2RFNmDCBJI+b1cVtO0t6S6WHSn9TMezoHCl0xpgkmsrck9ba58OL94d3pRL+fCC8vBBo/t/AHGDPEZZ/hbX2QWttvrU2Pysrq/3eiIhIhMXa9mvZsmUYbyqBLrlRWZ8/dzLB1EyCqZnUDTsDf+7kqKwXoCFrKH5/Pf/+97+jts6OJjU1lePGjmVVadumL1ldnITL5WLSpI59QgQ4c5arAR4GNlhr/9jsrsXAwTNV5wIvNlv+/fDZrpOBivCu2deAmcaYruEzYmeGl4mISASEQiFWrFyJv1NOQh4792WhtCyMN4UVK1Y4HSWhTZ06jX01hr01R/87tbo0mTGjR3fo6UoOcuJf5FTge8DJxpjV4Y8zgDuB04wxm4HTwrcBXgG2AgXAQ8BVAOGTIf4L+Dj8ccvBEyRERKT9lZaWUltTQygt0+ko0WEMjcnd2LZ9u9NJEtrB3aWrSo5ut2tRnYtdVS6mTJ0aiVhxx4mzXP9Ny8e/AZzSwuMtcPVhXusR4JH2SyciIodTVdV0trx1+xxOEj3W46Oy4kiHecux6tWrFwMH9GdVcQFn5Na3+nmrwsfdTZkyJULJ4kvij5mLiEi7SEtLA8AEO85lsUzAT3p6utMxEt6UqdPYXOGhprH1J9p8WuIlJ7sPOTk5EUwWP1ToRESkVbKyssjI6IS7+sDXPzgR2BBJdcUMHZp4kyfHmsmTJzdNQdLKq0b4g7ChPIkTpmh360EqdCIi0irGGE48cRre8p0QSPxROk/ZDmyjn6k6Rivihg8fTkZ6GmtKWlfoNpQlEQjp6hDNqdCJiEirnXfeedhQAN+e1U5HiaxQkJQ9q+jduw+TJ0dvqpSOyu1uuhbrmvJkbIszyn7RmtIkfN4kRo8eHflwcUKFTkREWm3QoEF8+1vfwntgPe7KvU7HiRjf7pVQV87111/X4a9AEC35+fmU18Pumq//fq8rS+a4sWPx+TrOCTpfR4VORESOyg9/+ENycnJI2/YvTH3inQHqKdmCd99avv3tb2uXXhRNmDABgPVlR97tWuY37KkxjB8/IRqx4oYKnYiIHJXU1FRuu/VWUr0e0je/jmmocTpSu3GX7yRl27uMGXNcQl3POx706tWLXj2y2Fh+5BnVNoYL37hx46IRK26o0ImIyFHr168ff/j93SSbRtI3vZIQI3We0q2kFrzFkCF53HHH7SQlte4AfWk/x40bz6YK3xGPo9tUkURqSjKDBw+OXrA4oEInIiJtMmzYMP50zz2kJ0HGpldwVRc5HanNkvavI2XLvxg1aiR//MMfDs25J9E1evRoqhpgX93h68nmCi8jRo7SsY1fokInIiJtNmzYMP58331kdckg/bNX8ZRuczrS0QmF8O14n+SdHzJ16lR+f/fdmkjYQSNHjgSgoKLl0dG6ABRWuw49Tj6nQiciIsekX79+3H//IoYOySNly9t4d39Cq+aecJhprCd182t4D2zgu9/9LrfccgvJyclOx+rQcnNzSUn2sa2y5ePotld5sMCIESOiGywOqNCJiMgx69atG/cuWMCsWbPw7VlNyuY3YnryYVdNMekbFuOrLeYXv/gFV111lXbhxQC3283QocPYWtXyCN3WcNEbOnRoNGPFBRU6ERFpF16vlxtvvJHrr78eb/UeMja8hKu21OlYX+Ep3kz6xlfIzEjhvvsWcvrppzsdSZoZMnQou6rdBEJfvW9HlYeePbrTpUuX6AeLcSp0IiLSbowxnH322dy7YAFdUjykb1yCp2SL07GahIL4tr9HyrZ3Oe640Tz814cYNmyY06nkS/Ly8mgMwd7ar46Y7qjxkjdEP7OWqNCJiEi7GzVqFA//9SFGjhhOytZ38O34AEItDLlEiWmoIW3Tq3iLNnHBBRfw+7vv1ihPjBo0aBAAu6q/WOj8QdhXaw7dL1+kQiciIhGRmZnJn+65h/POOw/vgfWkfrYU01gX9Rzuqn1kbFhMSqCS3/72t/zgBz/A4zny5LXinNzcXDxuN7uqv/gz2l3jxloYOHCgQ8limwqdiIhEjMfjYf78+dx0000k15eQvuElXDUlUVt/0oGNpG5aSq/u3Xjg/vs56aSTorZuaRuPx0Pfvjlfuabr7pqmgjdgwAAnYsU8FToREYm4mTNnct9995GZnkz6plfwlG2P7AptCN+OD0jesZyJ+fk8+MD99O/fP7LrlHbTf8BAdtd5v7BsT42bJI+bPn36OJQqtqnQiYhIVAwdOpQHH3yAIXmDSCl4i6S9ayIzX12wkZTNb+I9sJ45c+Zw5513kJGR0f7rkYjp168fxbUQavbrsafWTU5OjnaXH4YKnYiIRE1mZib3LljASSedRHLhx/h2fdiupc401pG2aSneqt38+Mc/5uqrr9b8cnEoNzcXCzSGzKFle+uS6Jvbz7lQMU6FTkREosrn83HzzTczZ84cvPvXk7z1nXY5A9b4q0nf9Aq+xgpuvfVWZs+e3Q5pxQk5OTkANASbbgdDUFRr6Nu3r4OpYpvGLUVEJOpcLhdXX301Xbp04aGHHsLYIHUDZ4CrbaNppr6S9M+WkuIOcfcf/8ioUaPaNa9EV3Z2NgANIQNYSvwugvbz5fJVKnQiDlm4cCEFBQURX8/BdVx33XURXc/gwYOZP39+RNchieeiiy4iOTmZhQsXkrL1HeoGzQBzdDuPjL+a9M2vkZZkuOePf2LIkCGRCStRk5aWRueMdBr9lQAcqGsq+joh4vBU6GJANP6w6496x5WSkuJ0BJEjOu+88wiFQvz5z3/Gt/09/P2ngTFf+zxoOmYuffNrpLiCKnMJplefPhRvryA3PUBRXVPJ7927t8OpYpcKXQehP+qxR8VX5HNz5syhqqqKxx57DOtNpyF73KH7QqndWn5SKEjqljdJCtRy1x/+oDKXYHr16k1l4SYuHlLLs1tScLlcZGZmOh0rZqnQxQD9YRcRgXnz5rF3716WLVtGMDWTYNdcAPy5k1t8vG/n+7iqDvDL3/yGMWPGRDOqREHPnj15v95gLZT43XTP7KopS45AZ7mKiEhMMMbw05/+lMF5eaTteBfjrz7sYz0lW/AWfcZFF13EjBkzohdSoiYrK4uGINQEDKX1LrJ69HQ6UkxToRNJcCUlJVx77bWUlETvcksibeXz+fjNzTfjdRmSdyxvcY4601hH6s4PGD5iBPPmzXMgpURD9+7dASjzuyhr9JCV1cPhRLEt7gudMWaWMWaTMabAGPNzp/OIxJpHH32UNWvW8NhjjzkdRaRVcnJyuPzyy/BUFOIp2/GV+327PsZFkJ/feKN2wSWwg8fLVTS4qGjQ8XNfJ64LnTHGDfwZ+CYwArjAGDPC2VQisaOkpISlS5dirWXp0qUapZO4cc4559A3N5eUPSvBfj7psKu2lKSSAr47Zw79+umqAYmsW7emk2GK6lzUNdpDt6VlcV3ogElAgbV2q7W2AXga0NTgImGPPvooofAM/MFgUKN0Ejc8Hg9XXH451FV8YZTOu3cNyckpXHjhhQ6mk2jo2rUrAIU17i/clpbFe6HLBnY1u10YXvYFxpgrjTErjDErioqKohZOxGlvvPEGgUAAgEAgwLJlyxxOJEerI2+/pk6dSq/evfEe2AA0HTuXVLaNs846k4yMDIfTSaSlpqaS5HGzu6Zpt3rnzp0dThTb4r3QtTTz5FeOoLXWPmitzbfW5mdlZUUhlkhsOPXUUw8dY+TxeDjttNMcTiRHqyNvv9xuN2eecQbuqn0YfzWe0m1gQ5xxxhlOR5MoMMaQkZHBvtqmqqJCd2TxXugKgeZX6s0B9jiURSTmzJ07F5er6Z+52+3m+9//vsOJRI7O9OnTAfBU7MJTsYs+2dkMHDjQ4VQSLZ07d6bU37TLtVOnTg6niW3xXug+BvKMMQOMMV7gfGCxw5lEYkZmZiazZs3CGMOsWbN0lpjEndzcXDK7Z+Gu3EtS9QEmTZzodCSJooyMTs2+1m72I4nrQmetDQDXAK8BG4BnrLXrnE0lElvmzp3L6NGjNToncckYw5jRo0gq244NNjJy5EinI0kUZTQblUtPT3cwSeyL60IHYK19xVo7xFo7yFp7m9N5RGJNZmYm9957r0bnJG4NGDDg0NeDBg1yMIlEW1paGgA+bxJJSUkOp4ltcV/oREQkseXk5Bz6Ojv7KxMZSAI7WOjSUlMdThL7VOhERCSmNT+71+fzOZhEoi01XORSVOi+lgqdiIjEtN69ewOQ3WykTjqGg4VOl3j7evoOiYhITOvevTvPPPPMoT/u0nGkpKQAYFxuh5PEPhU6ERGJeT169HA6gjggOTnZ6QhxQ7tcRUREJCbpmMnWU6ETERGRmKRC13oqdCIiIhKTvF6v0xHihgqdiIiIxCRNJtx6KnQiIiISkzRC13oqdCIiIhKT3G5NV9JaKnQiIiISkzShcOup0ImIiEhMcrlUU1pL3ykRERGROKdCJyIiIjGpc+fOAJxyyikOJ4l92jktIiIiMSkzM5MXXniBLl26OB0l5qnQiYiISMzq2rWr0xHigna5ioiIiMQ5FToRERGROKdCJyIiIhLnVOhERERE4pwKnYiIiEicU6ETERERiXMqdCIiIiJxzlhrnc4QVcaYImCH0zkc0h0odjqEOKIj/+z7WWuznA7RHrT96rC/wx1dR//Zt2ob1uEKXUdmjFlhrc13OodEn372Eu/0O9xx6WffOtrlKiIiIhLnVOhERERE4pwKXcfyoNMBxDH62Uu80+9wx6WffSvoGDoRERGROKcROhEREZE4p0LXARhjZhljNhljCowxP3c6j0SPMeYRY8wBY8xap7OItJW2YR2Ttl9HR4UuwRlj3MCfgW8CI4ALjDEjnE0lUfQ3YJbTIUTaStuwDu1vaPvVaip0iW8SUGCt3WqtbQCeBmY7nEmixFr7v0Cp0zlEjoG2YR2Utl9HR4Uu8WUDu5rdLgwvExGJB9qGibSCCl3iMy0s06nNIhIvtA0TaQUVusRXCPRtdjsH2ONQFhGRo6VtmEgrqNAlvo+BPGPMAGOMFzgfWOxwJhGR1tI2TKQVVOgSnLU2AFwDvAZsAJ6x1q5zNpVEizHmKeB9YKgxptAYc5nTmUSOhrZhHZe2X0dHV4oQERERiXMaoRMRERGJcyp0IiIiInFOhU5EREQkzqnQiYiIiMQ5FToRERGROKdCJyIiIhLnVOikQzLGuJ3OICLSFtp+SUs8TgcQiQRjzH8BxdbaBeHbtwH7gXOAvcBYYIRzCUVEWqbtl7SFJhaWhGSM6Q88b60db4xxAZuBnwGPAaOstdscjCcicljafklbaIROEpK1drsxpsQYMw7oCawCSoCPtDEUkVim7Ze0hQqdJLK/ApcAvYBHwstqHEsjItJ62n7JUdFJEZLIXgBmARNpurC3iEi80PZLjopG6CRhWWsbjDFvA+XW2qAxxulIIiKtou2XHC2dFCEJK3ww8SfAHGvtZqfziIi0lrZfcrS0y1USkjFmBFAAvKmNoYjEE22/pC00QiciIiIS5zRCJyIiIhLnVOhERERE4pwKnYiIiEicU6ETERERiXMqdCIiIiJxToVOREREJM79f8H4AwJ7N68TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c96274e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes=plt.subplots(1,2,sharey=True,figsize=(10,4))  #axes控制图片展示在哪个子图中\n",
    "sns.boxplot(x='yr',y=\"cnt\",data=df,ax=axes[0])\n",
    "sns.violinplot(x=\"yr\",y=\"cnt\",data=df,ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29c962b4eb8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c96363048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes=plt.subplots(1,2,sharey=True,figsize=(10,4))  #axes控制图片展示在哪个子图中\n",
    "sns.boxplot(x='season',y=\"cnt\",data=df,ax=axes[0])\n",
    "sns.violinplot(x=\"season\",y=\"cnt\",data=df,ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['cnt']\n",
    "X=df.drop('cnt',axis=1)\n",
    "\n",
    "log_y=np.log1p(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将dteday中的日期提取出来，看是否与最终的结果有关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\install\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "day=df['dteday']\n",
    "for i in range(len(day)):\n",
    "    day[i]=day[i][-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "离散型数据处理(season/ mnth/ weekday/ weathersit )  \n",
    "均为数值型，可以用get_dummies进行独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['season'].astype('object')\n",
    "x_season=X['season']\n",
    "x_season=pd.get_dummies(x_season,prefix='season')\n",
    "\n",
    "X['mnth'].astype('object')\n",
    "x_mnth=X['mnth']\n",
    "x_mnth=pd.get_dummies(x_mnth,prefix='mnth')\n",
    "\n",
    "X['weekday'].astype('object')\n",
    "x_weekday=X['weekday']\n",
    "x_weekday=pd.get_dummies(x_weekday,prefix='weekday')\n",
    "\n",
    "X['weathersit'].astype('object')\n",
    "x_weathersit=X['weathersit']\n",
    "x_weathersit=pd.get_dummies(x_weathersit,prefix='weathersit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=['season','mnth','weekday','weathersit','dteday']\n",
    "X=X.drop(features,axis=1)\n",
    "X['day']=day\n",
    "feat_name=X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "连续值标准化处理(StandardScaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\install\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:7: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  import sys\n",
      "D:\\software\\install\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\software\\install\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:8: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X=StandardScaler()\n",
    "ss_y=StandardScaler()\n",
    "ss_log_y=StandardScaler()\n",
    "\n",
    "X=ss_X.fit_transform(X)   #用训练数据训练\n",
    "y=ss_y.fit_transform(y.reshape(-1,1))\n",
    "log_y=ss_log_y.fit_transform(log_y.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_day=pd.DataFrame(data=X,columns=feat_name,index=df.index)\n",
    "fe_data=pd.concat([fe_day,x_season,x_mnth,x_weekday,x_weathersit],axis=1,ignore_index=False)\n",
    "\n",
    "fe_data['cnt']=y\n",
    "fe_data['log_cnt']=log_y\n",
    "fe_data.to_csv('FE_day.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.729683</td>\n",
       "      <td>-1.001369</td>\n",
       "      <td>-0.171981</td>\n",
       "      <td>-1.471225</td>\n",
       "      <td>-0.826662</td>\n",
       "      <td>-0.679946</td>\n",
       "      <td>1.250171</td>\n",
       "      <td>-0.387892</td>\n",
       "      <td>-0.753734</td>\n",
       "      <td>-1.925471</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.817953</td>\n",
       "      <td>-2.387564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.724944</td>\n",
       "      <td>-1.001369</td>\n",
       "      <td>-0.171981</td>\n",
       "      <td>-1.471225</td>\n",
       "      <td>-0.721095</td>\n",
       "      <td>-0.740652</td>\n",
       "      <td>0.479113</td>\n",
       "      <td>0.749602</td>\n",
       "      <td>-1.045214</td>\n",
       "      <td>-1.915209</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.912999</td>\n",
       "      <td>-2.742332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.720205</td>\n",
       "      <td>-1.001369</td>\n",
       "      <td>-0.171981</td>\n",
       "      <td>0.679706</td>\n",
       "      <td>-1.634657</td>\n",
       "      <td>-1.749767</td>\n",
       "      <td>-1.339274</td>\n",
       "      <td>0.746632</td>\n",
       "      <td>-1.061246</td>\n",
       "      <td>-1.556689</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.629925</td>\n",
       "      <td>-1.847887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.715466</td>\n",
       "      <td>-1.001369</td>\n",
       "      <td>-0.171981</td>\n",
       "      <td>0.679706</td>\n",
       "      <td>-1.614780</td>\n",
       "      <td>-1.610270</td>\n",
       "      <td>-0.263182</td>\n",
       "      <td>-0.389829</td>\n",
       "      <td>-1.078734</td>\n",
       "      <td>-1.412383</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.519898</td>\n",
       "      <td>-1.596254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.710728</td>\n",
       "      <td>-1.001369</td>\n",
       "      <td>-0.171981</td>\n",
       "      <td>0.679706</td>\n",
       "      <td>-1.467414</td>\n",
       "      <td>-1.504971</td>\n",
       "      <td>-1.341494</td>\n",
       "      <td>-0.046307</td>\n",
       "      <td>-1.116627</td>\n",
       "      <td>-1.371336</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.500269</td>\n",
       "      <td>-1.554995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    instant        yr   holiday  workingday      temp     atemp       hum  \\\n",
       "0 -1.729683 -1.001369 -0.171981   -1.471225 -0.826662 -0.679946  1.250171   \n",
       "1 -1.724944 -1.001369 -0.171981   -1.471225 -0.721095 -0.740652  0.479113   \n",
       "2 -1.720205 -1.001369 -0.171981    0.679706 -1.634657 -1.749767 -1.339274   \n",
       "3 -1.715466 -1.001369 -0.171981    0.679706 -1.614780 -1.610270 -0.263182   \n",
       "4 -1.710728 -1.001369 -0.171981    0.679706 -1.467414 -1.504971 -1.341494   \n",
       "\n",
       "   windspeed    casual  registered    ...     weekday_2  weekday_3  weekday_4  \\\n",
       "0  -0.387892 -0.753734   -1.925471    ...             0          0          0   \n",
       "1   0.749602 -1.045214   -1.915209    ...             0          0          0   \n",
       "2   0.746632 -1.061246   -1.556689    ...             0          0          0   \n",
       "3  -0.389829 -1.078734   -1.412383    ...             1          0          0   \n",
       "4  -0.046307 -1.116627   -1.371336    ...             0          1          0   \n",
       "\n",
       "   weekday_5  weekday_6  weathersit_1  weathersit_2  weathersit_3       cnt  \\\n",
       "0          0          1             0             1             0 -1.817953   \n",
       "1          0          0             0             1             0 -1.912999   \n",
       "2          0          0             1             0             0 -1.629925   \n",
       "3          0          0             1             0             0 -1.519898   \n",
       "4          0          0             1             0             0 -1.500269   \n",
       "\n",
       "    log_cnt  \n",
       "0 -2.387564  \n",
       "1 -2.742332  \n",
       "2 -1.847887  \n",
       "3 -1.596254  \n",
       "4 -1.554995  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 39 columns):\n",
      "instant         731 non-null float64\n",
      "yr              731 non-null float64\n",
      "holiday         731 non-null float64\n",
      "workingday      731 non-null float64\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "casual          731 non-null float64\n",
      "registered      731 non-null float64\n",
      "day             731 non-null float64\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "cnt             731 non-null float64\n",
      "log_cnt         731 non-null float64\n",
      "dtypes: float64(13), uint8(26)\n",
      "memory usage: 92.9 KB\n"
     ]
    }
   ],
   "source": [
    "fe_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 39 columns):\n",
      "instant         731 non-null float64\n",
      "yr              731 non-null float64\n",
      "holiday         731 non-null float64\n",
      "workingday      731 non-null float64\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "casual          731 non-null float64\n",
      "registered      731 non-null float64\n",
      "day             731 non-null float64\n",
      "season_1        731 non-null int64\n",
      "season_2        731 non-null int64\n",
      "season_3        731 non-null int64\n",
      "season_4        731 non-null int64\n",
      "mnth_1          731 non-null int64\n",
      "mnth_2          731 non-null int64\n",
      "mnth_3          731 non-null int64\n",
      "mnth_4          731 non-null int64\n",
      "mnth_5          731 non-null int64\n",
      "mnth_6          731 non-null int64\n",
      "mnth_7          731 non-null int64\n",
      "mnth_8          731 non-null int64\n",
      "mnth_9          731 non-null int64\n",
      "mnth_10         731 non-null int64\n",
      "mnth_11         731 non-null int64\n",
      "mnth_12         731 non-null int64\n",
      "weekday_0       731 non-null int64\n",
      "weekday_1       731 non-null int64\n",
      "weekday_2       731 non-null int64\n",
      "weekday_3       731 non-null int64\n",
      "weekday_4       731 non-null int64\n",
      "weekday_5       731 non-null int64\n",
      "weekday_6       731 non-null int64\n",
      "weathersit_1    731 non-null int64\n",
      "weathersit_2    731 non-null int64\n",
      "weathersit_3    731 non-null int64\n",
      "cnt             731 non-null float64\n",
      "log_cnt         731 non-null float64\n",
      "dtypes: float64(13), int64(26)\n",
      "memory usage: 222.8 KB\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('FE_day.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=['instant','casual','registered']\n",
    "df=df.drop(features,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('cnt',axis=1)\n",
    "y=df['cnt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练数据和测试数据分离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=0.2,random_state=42,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 35)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(147, 35)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实例化回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()\n",
    "rr=Ridge()   #默认alpha=1\n",
    "lsr=Lasso()   #默认alpha=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "回归模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,Y_train)\n",
    "rr.fit(X_train,Y_train)\n",
    "lsr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "回归模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr=lr.predict(X_test)\n",
    "y_pred_rr=rr.predict(X_test)\n",
    "y_pred_lsr=lsr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小二乘线性回归对应的RMSE为0.163\n",
      "岭回归对应的RMSE为0.161\n",
      "LASSO对应的RMSE为1.091\n"
     ]
    }
   ],
   "source": [
    "RMSE_lr=mean_squared_error(Y_test,y_pred_lr)\n",
    "RMSE_rr=mean_squared_error(Y_test,y_pred_rr)\n",
    "RMSE_lsr=mean_squared_error(Y_test,y_pred_lsr)\n",
    "print('最小二乘线性回归对应的RMSE为{:.3f}\\n岭回归对应的RMSE为{:.3f}\\nLASSO对应的RMSE为{:.3f}'.format(RMSE_lr,RMSE_rr,RMSE_lsr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据结果可以看出岭回归对应的RMSE值最小，预测结果最为准确；  \n",
    "Lasso的预测结果精确度最低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比较三种模型的系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小二乘线性回归的系数为：[ 0.21795103  0.00077371 -0.00743694  0.08208826 -0.02349015 -0.03452676\n",
      " -0.04048164  0.00693351 -0.0268441   0.0388829  -0.05692791  0.04488911\n",
      " -0.12396709 -0.26527594 -0.04008207 -0.04877096  0.06325816  0.12185868\n",
      "  0.06560776  0.15260577  0.26600415  0.1342837  -0.16419206 -0.16133008\n",
      " -0.06557066 -0.04751357 -0.01615435  0.03256018  0.02893181 -0.00115238\n",
      "  0.06889896  0.04709951 -0.0671103   0.02001079  0.73376143]\n",
      "截距为：-0.015150243482005919\n",
      "岭回归的系数为：[ 2.19688922e-01 -4.85110328e-05 -7.47557489e-03  8.96591469e-02\n",
      " -1.67187621e-02 -3.59002220e-02 -4.10072066e-02  6.57062710e-03\n",
      " -4.76834215e-02  4.40071299e-02 -3.87038637e-02  4.23801553e-02\n",
      " -9.05817683e-02 -2.30354429e-01 -2.27788081e-02 -5.09801818e-02\n",
      "  5.29529169e-02  9.94089245e-02  2.95065304e-02  1.18951286e-01\n",
      "  2.43030052e-01  1.37867595e-01 -1.48290135e-01 -1.38731982e-01\n",
      " -6.49791179e-02 -4.65198188e-02 -1.68491043e-02  3.22177151e-02\n",
      "  2.75694395e-02  9.81579202e-05  6.84627286e-02  4.92391342e-02\n",
      " -6.44848696e-02  1.52457354e-02  7.26871086e-01]\n",
      "截距为：-0.01737733114449865\n",
      "Lasso的系数为：[ 0. -0.  0.  0.  0. -0. -0. -0. -0.  0.  0.  0. -0. -0. -0. -0.  0.  0.\n",
      "  0.  0.  0.  0. -0. -0. -0. -0. -0. -0.  0.  0.  0.  0. -0. -0.  0.]\n",
      "截距为：0.029252189333695656\n"
     ]
    }
   ],
   "source": [
    "print('最小二乘线性回归的系数为：{}\\n截距为：{}'.format(lr.coef_,lr.intercept_))\n",
    "print('岭回归的系数为：{}\\n截距为：{}'.format(rr.coef_,rr.intercept_))\n",
    "print('Lasso的系数为：{}\\n截距为：{}'.format(lsr.coef_,lsr.intercept_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>系数结果分析：  \n",
    "由这些系数可以看出，当alpha设为1时，由于正则项，系数大小对损失函数的结果影响很大，导致lasso的所有系数均为0，这也是后面计算得到的lasso的RMSE值很大的原因  \n",
    "而岭回归中由于有L2正则项具有系数收缩的作用，所以岭回归的系数与最小二乘相比，更趋近于0。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color ='red'> 修改alpha值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha=0.01,岭回归系数为[ 0.21796927  0.0007638  -0.00743822  0.08220871 -0.02343494 -0.03454365\n",
      " -0.04048822  0.00692919 -0.02710917  0.03895754 -0.05669411  0.04484573\n",
      " -0.12353514 -0.2648343  -0.03986166 -0.04881177  0.0631174   0.12156707\n",
      "  0.06514538  0.15217549  0.26571905  0.13434475 -0.1639876  -0.16103867\n",
      " -0.06556381 -0.04750148 -0.01616359  0.03255695  0.02891532 -0.00113775\n",
      "  0.06889436  0.04712263 -0.06707983  0.0199572   0.73368569]，截距为-0.01517564915967733\n",
      "\n",
      "\n",
      "alpha=0.01,Lasso回归系数为[ 0.17931302 -0.         -0.          0.10852407  0.         -0.01364788\n",
      " -0.02201655  0.         -0.0197398   0.          0.          0.\n",
      "  0.         -0.04467574  0.         -0.          0.          0.\n",
      " -0.          0.          0.03931017  0.04167531 -0.         -0.\n",
      " -0.         -0.         -0.          0.          0.         -0.\n",
      "  0.02226399  0.         -0.06385663  0.          0.79216423]，截距为0.013864255981018456\n",
      "\n",
      "\n",
      "alpha=0.05,岭回归系数为[ 2.18042031e-01  7.24612872e-04 -7.44311571e-03  8.26778924e-02\n",
      " -2.32097922e-02 -3.46104091e-02 -4.05141474e-02  6.91210264e-03\n",
      " -2.81550107e-02  3.92497820e-02 -5.57720273e-02  4.46772560e-02\n",
      " -1.21832566e-01 -2.63091426e-01 -3.89920520e-02 -4.89699070e-02\n",
      "  6.25644530e-02  1.20418202e-01  6.33219060e-02  1.50478482e-01\n",
      "  2.64592762e-01  1.34582087e-01 -1.63181805e-01 -1.59890136e-01\n",
      " -6.55366641e-02 -4.74537150e-02 -1.61999735e-02  3.25439842e-02\n",
      "  2.88500717e-02 -1.07974442e-03  6.88760411e-02  4.72147269e-02\n",
      " -6.69590382e-02  1.97443113e-02  7.33384433e-01]，截距为-0.01527649802821449\n",
      "\n",
      "\n",
      "alpha=0.05,Lasso回归系数为[ 0.12630728 -0.         -0.          0.06461246  0.         -0.\n",
      " -0.          0.         -0.          0.          0.          0.\n",
      " -0.         -0.         -0.         -0.          0.          0.\n",
      " -0.          0.          0.          0.         -0.         -0.\n",
      " -0.         -0.         -0.          0.          0.         -0.\n",
      "  0.          0.         -0.          0.          0.82537605]，截距为-0.004315211450336785\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\software\\install\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  if __name__ == '__main__':\n",
      "D:\\software\\install\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  positive)\n",
      "D:\\software\\install\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "RMSE_rr1=[]\n",
    "RMSE_lsr1=[]\n",
    "for i in range(100):\n",
    "    i/=100\n",
    "    rr_1=Ridge(alpha=i)\n",
    "    lsr_1=Lasso(alpha=i)\n",
    "\n",
    "    rr_1.fit(X_train,Y_train)\n",
    "    lsr_1.fit(X_train,Y_train)\n",
    "\n",
    "    y_pred_rr1=rr_1.predict(X_test)\n",
    "    y_pred_lsr1=lsr_1.predict(X_test)\n",
    "\n",
    "    RMSE_rr1.append([i,mean_squared_error(Y_test,y_pred_rr1)])\n",
    "    RMSE_lsr1.append([i,mean_squared_error(Y_test,y_pred_lsr1)])\n",
    "    if i==0.05 or i==0.01:\n",
    "        print('alpha={},岭回归系数为{}，截距为{}\\n\\n'.format(i,rr_1.coef_,rr_1.intercept_))\n",
    "        print('alpha={},Lasso回归系数为{}，截距为{}\\n\\n'.format(i,lsr_1.coef_,lsr_1.intercept_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由Lasso的系数可以知道，哪些特征对于模型训练更为重要一些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " [0.99, 0.1606942734460612]]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE_rr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K7jBxInz3u7DlljBnjoJd8oPCXaSF1q4N4+tnnx1Wc3z1Vdh//9hViQQKd5EWWLYM/vM/w/j61VeHhcC++tXYVYl8QWPuIs300ENhKYEttoCyMujfP3ZFIl+mnrtImtatg4sugh/8ALp2hddeU7BL/lLPXSQNy5bBoEFQXg4jRsCtt8LWW8euSqRhCneRJkydCsOHQ5s28Kc/hZ67SL7TsIxIA1avDmPrgwdD9+7wxhsKdikcCneRerz8ctj67r774Jpr4MUXtYyAFBaFu0gt69eHMD/kkHD/hRfguuu0sYYUHv3IiqQsXAg//nG4GOn002HsWNh++9hVibSMeu5S9DZuhFtuCcMw774bFvy65x4FuxQ29dylqC1aBGeeCXPnwgknwPjx8LWvxa5KpPXS6rmbWV8zW2xmS83s8kbanWRmbmalmStRJPPWr4cxY+CAA2DJEnjggTDNUcEuSdFkz93M2gDjgKOBSmCemZW5+4I67bYDfga8nI1CRTKlvByGDoX588MKjmPHKtQledLpufcClrr72+6+DpgKDKyn3fXAzcDaDNYnkjGffgoXXAAHHwwffBDWiJk+XcEuyZROuO8GVNR6XJl67nNm1hPo4u6PZrA2kYxwhwcfhH33hdtvh3POgQULYGB9XRSRhEgn3K2e5/zzF822AG4DLmryjcyGm1m5mZVXVVWlX6VICy1eDMccE4Zfdt45nDgdN04zYST50gn3SqBLrcedgeW1Hm8H9ACeN7N3gd5AWX0nVd19oruXuntpx44dW161SBNWr4bLL4f99gtb4I0dC/PmQa9esSsTyY10pkLOA7qa2Z7Ae8Ag4LTNL7p7NdBh82Mzex642N3LM1uqSNM2bYIpU+DKK2HlyrA2zE03QadOsSsTya0me+7uvgEYATwFLASmu/tbZjbazAZku0CRdM2cCQcdFGbC7LVXWB/m7rsV7FKc0rqIyd0fBx6v89w1DbQ9ovVliaTvrbfgssvgscegSxf4/e/DSo5W39kikSKh5QekYL37bri69Fvfglmz4Ne/DhcknXaagl1Eyw9IwVmxAm68ESZMCPuYXnhhGGPfaafYlYnkD4W7FIwVK+Dmm0Oor18fxtavvho6d45dmUj+UbhL3quoCHuWTpwYQv3002HUKPj612NXJpK/FO6StxYvDj31++4LV5kOGRJCfe+9Y1cmkv8U7pJX3MPJ0VtvhbIyaNcOzj4bLrkEdt89dnUihUPhLnmhpgZmzIDf/S6s2rjTTmG7u/PPD8sGiEjzKNwlqooKuOuuMJ6+ciV885twxx1wxhnQvn3s6kQKl8Jdcm7DBnjyyRDojz0WhmKOPTYsx9unT5jeKCKto3CXnFm8OKz7cs89YVpjp05hca9hw6CkJHZ1IsmicJesWrkSpk2D++8PqzK2aQP9+sFZZ8Fxx8GWW8auUCSZFO6ScVVVYT/S6dPh+efDSo09e8JvfhPWfNlll9gViiSfwl0yYtkyePjhsHXd7Nkh0PfZJywLcOqp0KNH7ApFiovCXVrks8/CfPQnnoDHHw/j6RAW8brqKjjxxHBfC3iJxKFwl7TU1IT55y+8AM89F3rnNTWw9dZwxBFw7rnQv39YR11E4lO4S73efz9sdjF3Lrz0Uri/dm14bf/9w8VFRx0Vgl3z0UXyj8K9yK1fD3//e9jwYv58eP31cFue2iW3bVs44IDQMz/sMDj0UOjQofH3FJH4FO4J5w7V1fDee2Fzi3/8A95+OwT6kiXhROj69aHtFlvAvvuGHnnPnnDwweHP//iPqIcgIi2gcC8wa9aEYZL33w/DJDU18M9/wurV4fbJJ/DBB/Dhh7BqVbhY6LPP/v092rULKyvuuy8MHBhmsnTvHi79V5CLJIPCPc+tXh3GvWfNChtAz537RU+7NjPYbjvYYYew6FaHDuHk5i67wK67hltJCeyxR7gyVJf4iySbwj2P1NTAggVhVsq8eeE2f36YM24GBx4Y1l858sjQ827XLty22Sac1NS0QxHZTOEewb/+Fca9Fy+GRYtg4cIQ4osWhUW1AHbcEUpLw5zxQw6B3r3hK1+JW7eIFA6Fe4bU1MCnn4aTl5vHvauqwtoq770XbhUV8M474bnadt8d9tsPBgwIF/58+9thCzn1xEWkpRTuLbBpU7gy88knwzDKggXhBGdDttkmbOLcufMXF/rsuWc4gdm1a3hdRCSTFO7NsG4d/OEPcMstYV74ttuGWSbHHhsCe4cdYPvtw61jx3BSc+edw3CKeuEikksK9zSsWBE2lrjzznB/v/3Cps2nnqola0UkPyncG7BpU5h6OHFiWL52w4bQQ588Gfr2VU9cRPKbwr2OioqwscTkyeHqzR13hJEj4bzzwvRDEZFCoHAnzG556KEQ6n/+c7hk/7DDYPTosHRtu3axKxQRaZ6iDfcPPwybM8+YAU89Fa763Gsv+OUvYciQMBVRRKRQFU24u8Obb4bpi4888sVuQV26hGGXU06BXr00li4iyZDYcHcPY+Z/+UvYx/OZZ76Yi77//mH7t/79w1WgWmdFRJImceFeXQ3jxsH48VBZGZ7r0CEsY3vMMXD00eFiIhGRJEtMuK9dCzfcALffHgL+mGPCuiyHHRauBNVwi4gUk7TC3cz6Ar8D2gCT3P2mOq//AvgpsAGoAs5y939kuNYGucPw4eHCopNOCkMuPXvm6tNFRPJPk6PNZtYGGAccC3QDBptZtzrNXgdK3f1bwIPAzZkutDETJoRgv+66MPtFwS4ixS6dU4m9gKXu/ra7rwOmAgNrN3D3me6+JvVwLpCzUe25c8Ma5/36hWEYERFJL9x3AypqPa5MPdeQocAT9b1gZsPNrNzMyquqqtKvsgFVVWEYpkuXcAGSZr2IiATpjLnXdyrS621oNgQoBQ6v73V3nwhMBCgtLa33PZpjzJiwNvq8eWGZABERCdIJ90qgS63HnYHldRuZWR9gFHC4u9dkpryGffQR3HUXnHYaHHBAtj9NRKSwpDOQMQ/oamZ7mtlWwCCgrHYDM+sJ3AkMcPdVmS/zy8aPD9vVXXxxLj5NRKSwNBnu7r4BGAE8BSwEprv7W2Y22swGpJrdAmwLzDCzN8ysrIG3y4jPPoOxY8MSvPvtl81PEhEpTGnNc3f3x4HH6zx3Ta37fTJcV6PuvRdWrYJLL83lp4qIFI6Cm1+ycSPceiscdBAcXu9pWxERKbjlBx56CJYuhRnXvoX9YlKY6L5p0xcNzL5Ya6D2n7XXH6jv+XTb1P6zodcbe++mPqM19WWqptbWmu7zLa0j1/Wl+9kx685EHS2pL91aG/qa1tSU7mc392tzUd+ee0KnTmRTwYW7PfcsR2/Zhh9c2we2agu9e0P79uFF93DbfL/ucw09v/l+Y19b98+GXm/ovetr09z6mvraTNTU2lrTfb6ldWSyPpFYxo+Hc87J6kcUXLifOGADJ66+BwZOC6uDbbdd7JKkkDX1n0/t1xr7j6WpNtn8TzMTdbSkvnRrbehrWlNTup/d3K/NRX0A3equ4JJ5BRfu9O0bbiKZ0NCv0yIFruBOqIqISNMU7iIiCaRwFxFJIIW7iEgCKdxFRBJI4S4ikkAKdxGRBFK4i4gkkHmkS7HNrAr4Rwu/vAPwQQbLKRTFeNzFeMxQnMddjMcMzT/uPdy9Y1ONooV7a5hZubuXxq4j14rxuIvxmKE4j7sYjxmyd9walhERSSCFu4hIAhVquE+MXUAkxXjcxXjMUJzHXYzHDFk67oIccxcRkcYVas9dREQakdfhbmZ9zWyxmS01s8vreX1rM5uWev1lMyvJfZWZlcYx/8LMFpjZfDN7zsz2iFFnpjV13LXanWRmbmYFP6sinWM2s1NS3++3zOyBXNeYDWn8jO9uZjPN7PXUz3m/GHVmkpn9j5mtMrM3G3jdzGxs6u9kvpkd2OoPdfe8vAFtgGXAXsBWwF+BbnXanAdMSN0fBEyLXXcOjvlIoH3q/rmFfszpHneq3XbAX4C5QGnsunPwve4KvA7smHq8c+y6c3TcE4FzU/e7Ae/GrjsDx30YcCDwZgOv9wOeAAzoDbzc2s/M5557L2Cpu7/t7uuAqcDAOm0GAvek7j8IHGVW0NvpNHnM7j7T3dekHs4FOue4xmxI53sNcD1wM7A2l8VlSTrHPAwY5+4fA7j7qhzXmA3pHLcDX0nd3x5YnsP6ssLd/wJ81EiTgcC9HswFdjCzXVrzmfkc7rsBFbUeV6aeq7eNu28AqoGdclJddqRzzLUNJfxvX+iaPG4z6wl0cfdHc1lYFqXzvd4H2MfMZpvZXDNLwv6S6Rz3tcAQM6sEHgdG5qa0qJr7b79J+byHan098LpTe9JpU0jSPh4zGwKUAodntaLcaPS4zWwL4DbgJ7kqKAfS+V63JQzNHEH4De1FM+vh7p9kubZsSue4BwNT3P03ZvYd4L7UcW/KfnnRZDzL8rnnXgl0qfW4M1/+9ezzNmbWlvArXGO/+uS7dI4ZM+sDjAIGuHtNjmrLpqaOezugB/C8mb1LGJMsK/CTqun+fD/s7uvd/R1gMSHsC1k6xz0UmA7g7i8B7QjrryRZWv/2myOfw30e0NXM9jSzrQgnTMvqtCkDzkjdPwn4s6fOThSoJo85NTxxJyHYkzAGC00ct7tXu3sHdy9x9xLCuYYB7l4ep9yMSOfn+yHCCXTMrANhmObtnFaZeekc9/8BRwGY2b6EcK/KaZW5Vwacnpo10xuodvcVrXrH2GeRmzjD3A9YQji7Pir13GjCP2wI3/QZwFLgFWCv2DXn4JifBVYCb6RuZbFrzsVx12n7PAU+WybN77UBvwUWAH8DBsWuOUfH3Q2YTZhJ8wbw/dg1Z+CY/wCsANYTeulDgXOAc2p9r8el/k7+lomfb12hKiKSQPk8LCMiIi2kcBcRSSCFu4hIAincRUQSSOEuIpJACncpSmb2bmrueKvaiOQrhbuISAIp3CXxzOwhM3s1tSb68DqvlZjZIjO7J7WO9oNm1r5Wk5Fm9pqZ/c3Mvpn6ml5mNie13vgcM/tGTg9IJA0KdykGZ7n7twkLrf3MzOquHPoNYKK7fwv4lLBPwGYfuPuBwHjg4tRzi4DD3L0ncA0wJqvVi7SAwl2Kwc/M7K+ENWm68OXFtyrcfXbq/v3AobVe+1Pqz1eBktT97YEZqV11bgO6Z6NokdZQuEuimdkRQB/gO+6+P2Fno3Z1mtVdg6P2482rbm7kiyWyrwdmunsPoH897ycSncJdkm574GN3X5MaM+9dT5vdU+uGQ1hLfFYa7/le6v5PMlKlSIYp3CXpngTamtl8Qo97bj1tFgJnpNp8lTC+3pibgRvNbDZhT1CRvKNVIaWomVkJ8GhqiEUkMdRzFxFJIPXcRUQSSD13EZEEUriLiCSQwl1EJIEU7iIiCaRwFxFJIIW7iEgC/T9ISa/f/mQDDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29c98034828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "plot1=plt.plot([x[0] for x in RMSE_rr1],[x[1] for x in RMSE_rr1],'r')\n",
    "plot2=plt.plot([x[0] for x in RMSE_lsr1],[x[1] for x in RMSE_lsr1],'b')\n",
    "plt.xlabel('alpha')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>上图为alpha取不同值时，岭回归和Lasso对应的RMSE。蓝色的线为Lasso对应RMSE，红色的线为岭回归对应的RMSE  \n",
    "可以看出，当不断减小的过程中，岭回归的对应的RMSE基本保持不变，而Lasso对应的RMSE快速下降，并最终的alpha=0时，与岭回归保持一致。  \n",
    "Lasso的RMSE值急速下降是因为在alpha减小的过程中，L1正则项的约束变小，所以等于0的系数的数量也不断减小，Lasso的结果更准确。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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